expose averaage runtime to report

main.py

@@ -30,6 +30,12 @@ DEFAULTS = [
 ]
 
 
+@dataclass
+class InterrogatorPost:
+    tags: List[str]
+    time_taken: float
+
+
 @dataclass
 class Interrogator:
     model_id: str
@@ -38,14 +44,14 @@ class Interrogator:
     def _process(self, lst):
         return lst
 
-    async def fetch_tags(self, ctx, md5_hash):
+    async def fetch(self, ctx, md5_hash):
         rows = await ctx.db.execute_fetchall(
-            "select output_tag_string from interrogated_posts where md5 = ? and model_name = ?",
+            "select output_tag_string, time_taken from interrogated_posts where md5 = ? and model_name = ?",
             (md5_hash, self.model_id),
         )
         assert len(rows) == 1  # run 'fight' mode, there's missing posts
-        tag_string = rows[0][0]
-        return self._process(tag_string.split())
+        tag_string, time_taken = rows[0][0], rows[0][1]
+        return InterrogatorPost(self._process(tag_string.split()), time_taken)
 
 
 class DDInterrogator(Interrogator):
@@ -318,6 +324,7 @@ async def scores(ctx):
 
     # absolute_scores = defaultdict(decimal.Decimal)
     model_scores = defaultdict(dict)
+    runtimes = defaultdict(list)
     incorrect_tags_counters = defaultdict(Counter)
 
     for md5_hash in all_hashes:
@@ -325,7 +332,9 @@ async def scores(ctx):
         post = await fetch_post(ctx, md5_hash)
         danbooru_tags = set(post["tag_string"].split())
         for interrogator in interrogators:
-            interrogator_tags = set(await interrogator.fetch_tags(ctx, md5_hash))
+            post_data = await interrogator.fetch(ctx, md5_hash)
+            runtimes[interrogator.model_id].append(post_data.time_taken)
+            interrogator_tags = set(post_data.tags)
             tagging_score, incorrect_tags = score(danbooru_tags, interrogator_tags)
             for tag in incorrect_tags:
                 incorrect_tags_counters[interrogator.model_id][tag] += 1
@@ -352,7 +361,8 @@ async def scores(ctx):
         key=lambda model: normalized_scores[model],
         reverse=True,
     ):
-        print(model, normalized_scores[model])
+        average_runtime = sum(runtimes[model]) / len(runtimes[model])
+        print(model, normalized_scores[model], "runtime", average_runtime, "sec")
         if os.getenv("SHOWOFF", "0") == "1":
             print("[", end="")