Add third party dependency version checker.

mindinsight/mindconverter/graph_based_converter/common/utils.py

@@ -109,3 +109,29 @@ def save_code_file_and_report(model_name: str, code_lines: Mapping[str, Tuple],
             log.error(str(error))
             log.exception(error)
             raise error
+
+
+def lib_version_satisfied(current_ver: str, mini_ver_limited: str,
+                          newest_ver_limited: str = ""):
+    """
+    Check python lib version whether is satisfied.
+
+    Notes:
+        Version number must be format of x.x.x, e.g. 1.1.0.
+
+    Args:
+        current_ver (str): Current lib version.
+        mini_ver_limited (str): Mini lib version.
+        newest_ver_limited (str): Newest lib version.
+
+    Returns:
+        bool, true or false.
+    """
+    required_version_number_len = 3
+    if len(list(current_ver.split("."))) != required_version_number_len or \
+            len(list(mini_ver_limited.split("."))) != required_version_number_len or \
+            (newest_ver_limited and len(newest_ver_limited.split(".")) != required_version_number_len):
+        raise ValueError("Version number must be format of x.x.x.")
+    if current_ver < mini_ver_limited or (newest_ver_limited and current_ver > newest_ver_limited):
+        return False
+    return True

mindinsight/mindconverter/graph_based_converter/constant.py

@@ -37,6 +37,10 @@ DYNAMIC_SHAPE = -1
 SCALAR_WITHOUT_SHAPE = 0
 UNKNOWN_DIM_VAL = "unk__001"
 
+ONNX_MIN_VER = "1.8.0"
+TF2ONNX_MIN_VER = "1.7.1"
+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'
 

mindinsight/mindconverter/graph_based_converter/framework.py

@@ -20,8 +20,9 @@ from importlib import import_module
 from importlib.util import find_spec
 
 import mindinsight
+from mindinsight.mindconverter.graph_based_converter.common.utils import lib_version_satisfied
 from mindinsight.mindconverter.graph_based_converter.constant import BINARY_HEADER_PYTORCH_FILE, FrameworkType, \
-    BINARY_HEADER_PYTORCH_BITS
+    BINARY_HEADER_PYTORCH_BITS, ONNX_MIN_VER, TF2ONNX_MIN_VER, ONNXRUNTIME_MIN_VER
 from mindinsight.mindconverter.graph_based_converter.mapper import ONNXToMindSporeMapper
 from mindinsight.mindconverter.common.log import logger as log
 from mindinsight.mindconverter.common.exceptions import GraphInitFail, TreeCreateFail, SourceFilesSaveFail, \
@@ -92,11 +93,30 @@ def tf_installation_validation(func):
            output_folder: str, report_folder: str = None,
            input_nodes: str = None, output_nodes: str = None):
         # Check whether tensorflow is installed.
-        if not find_spec("tensorflow") or not find_spec("tf2onnx") or not find_spec("onnxruntime"):
-            error = ModuleNotFoundError("Tensorflow and tf2onnx are required when using "
-                                        "graph based scripts converter.")
+        if not find_spec("tensorflow") or not find_spec("tf2onnx") or not find_spec("onnx") \
+                or not find_spec("onnxruntime"):
+            error = ModuleNotFoundError(
+                f"TensorFlow, tf2onnx(>={TF2ONNX_MIN_VER}), onnx(>={ONNX_MIN_VER}) and "
+                f"onnxruntime(>={ONNXRUNTIME_MIN_VER}) are required when using graph "
+                f"based scripts converter for TensorFlow conversion."
+            )
             log.error(str(error))
             raise error
+
+        onnx, tf2onnx = import_module("onnx"), import_module("tf2onnx")
+        ort = import_module("onnxruntime")
+
+        if not lib_version_satisfied(getattr(onnx, "__version__"), ONNX_MIN_VER) \
+                or not lib_version_satisfied(getattr(ort, "__version__"), ONNXRUNTIME_MIN_VER) \
+                or not lib_version_satisfied(getattr(tf2onnx, "__version__"), TF2ONNX_MIN_VER):
+            error = ModuleNotFoundError(
+                f"TensorFlow, tf2onnx(>={TF2ONNX_MIN_VER}), onnx(>={ONNX_MIN_VER}) and "
+                f"onnxruntime(>={ONNXRUNTIME_MIN_VER}) are required when using graph "
+                f"based scripts converter for TensorFlow conversion."
+            )
+            log.error(str(error))
+            raise error
+
         func(graph_path=graph_path, sample_shape=sample_shape,
              output_folder=output_folder, report_folder=report_folder,
              input_nodes=input_nodes, output_nodes=output_nodes)