Merge branch 'fix-converter-bugs' into 'master'

fix converter bugs

See merge request !1182

tools/python/convert.py

@@ -176,13 +176,28 @@ def convert_model(conf):
     input_count = len(input_tensors)
     input_data_types = [data_type_map[dt] for dt in
                         to_list(conf.get("input_data_types",
-                                         ["float32"] * input_count))]
+                                         ["float32"]))]
+    if len(input_data_types) == 1 and input_count > 1:
+        input_data_types = [input_data_types[0]] * input_count
+    mace_check(len(input_data_types) == input_count,
+               "the number of input_data_types should be "
+               "the same as input tensors")
     input_data_formats = [data_format_map[df] for df in
                           to_list(conf.get("input_data_formats",
-                                           ["NHWC"] * input_count))]
+                                           ["NHWC"]))]
+    if len(input_data_formats) == 1 and input_count > 1:
+        input_data_formats = [input_data_formats[0]] * input_count
+    mace_check(len(input_data_formats) == input_count,
+               "the number of input_data_formats should be "
+               "the same as input tensors")
     input_ranges = [parse_float_array_from_str(r) for r in
                     to_list(conf.get("input_ranges",
-                                     ["-1.0,1.0"] * input_count))]
+                                     ["-1.0,1.0"]))]
+    if len(input_ranges) == 1 and input_count > 1:
+        input_ranges = [input_ranges[0]] * input_count
+    mace_check(len(input_ranges) == input_count,
+               "the number of input_ranges should be "
+               "the same as input tensors")
     for i in range(len(input_tensors)):
         input_node = cvt.NodeInfo()
         input_node.name = input_tensors[i]
@@ -204,10 +219,20 @@ def convert_model(conf):
     output_count = len(output_tensors)
     output_data_types = [data_type_map[dt] for dt in
                          to_list(conf.get("output_data_types",
-                                          ["float32"] * output_count))]
+                                          ["float32"]))]
+    if len(output_data_types) == 1 and output_count > 1:
+        output_data_types = [output_data_types[0]] * output_count
+    mace_check(len(output_data_types) == output_count,
+               "the number of output_data_types should be "
+               "the same as output tensors")
     output_data_formats = [data_format_map[df] for df in
                            to_list(conf.get("output_data_formats",
-                                            ["NHWC"] * output_count))]
+                                            ["NHWC"]))]
+    if len(output_data_formats) == 1 and output_count > 1:
+        output_data_formats = [output_data_formats[0]] * output_count
+    mace_check(len(output_data_formats) == output_count,
+               "the number of output_data_formats should be "
+               "the same as output tensors")
     for i in range(len(output_tensors)):
         output_node = cvt.NodeInfo()
         output_node.name = output_tensors[i]

tools/python/utils/config_parser.py

@@ -46,7 +46,7 @@ CPP_KEYWORDS = [
 def sanitize_load(s):
     # do not let yaml parse ON/OFF to boolean
     for w in ["ON", "OFF", "on", "off"]:
-        s = re.sub(r":\s+" + w, r": '" + w + "'", s)
+        s = re.sub(r":\s+" + w + "$", r": '" + w + "'", s)
 
     # sub ${} to env value
     s = re.sub(r"\${(\w+)}", lambda x: os.environ[x.group(1)], s)