update stat to classify papers (#267)

docs/stat.py

 #!/usr/bin/env python
+import functools as func
 import glob
+import numpy as np
 import re
 from os import path as osp
 
@@ -17,26 +19,40 @@ for f in files:
     with open(f, 'r') as content_file:
         content = content_file.read()
 
-    title = content.split('\n')[0].replace('#', '')
+    title = content.split('\n')[0].replace('#', '').strip()
     ckpts = set(x.lower().strip()
                 for x in re.findall(r'https?://download.*\.pth', content)
                 if 'mmdetection3d' in x)
     if len(ckpts) == 0:
         continue
 
+    _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)]
+    assert len(_papertype) > 0
+    papertype = _papertype[0]
+
+    paper = set([(papertype, title)])
+
     titles.append(title)
     num_ckpts += len(ckpts)
     statsmsg = f"""
-\t* [{title}]({url}) ({len(ckpts)} ckpts)
+\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts)
 """
-    stats.append((title, ckpts, statsmsg))
+    stats.append((paper, ckpts, statsmsg))
 
+allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
 msglist = '\n'.join(x for _, _, x in stats)
 
+papertypes, papercounts = np.unique([t for t, _ in allpapers],
+                                    return_counts=True)
+countstr = '\n'.join(
+    [f'   - {t}: {c}' for t, c in zip(papertypes, papercounts)])
+
 modelzoo = f"""
 \n## Model Zoo Statistics
 
 * Number of papers: {len(set(titles))}
+{countstr}
+
 * Number of checkpoints: {num_ckpts}
 {msglist}
 """