Update stat to classify papers (#139)

* update stat to classify papers

* modify tag of papers

configs/lenet/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-[BACKBONE]
+[ALGORITHM]
 
 ```latex
 @ARTICLE{6795724,

configs/mobilenet_v2/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-[BACKBONE]
+[ALGORITHM]
 
 ```latex
 @INPROCEEDINGS{8578572,

configs/resnet/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-[BACKBONE]
+[ALGORITHM]
 
 ```latex
 @inproceedings{he2016deep,

configs/resnext/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-[BACKBONE]
+[ALGORITHM]
 
 ```latex
 @inproceedings{xie2017aggregated,

configs/seresnet/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-[BACKBONE]
+[ALGORITHM]
 
 ```latex
 @inproceedings{hu2018squeeze,

configs/seresnext/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-[BACKBONE]
+[ALGORITHM]
 
 ```latex
 @inproceedings{hu2018squeeze,

configs/shufflenet_v1/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-[BACKBONE]
+[ALGORITHM]
 
 ```latex
 @inproceedings{zhang2018shufflenet,

configs/shufflenet_v2/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-[BACKBONE]
+[ALGORITHM]
 
 ```latex
 @inproceedings{ma2018shufflenet,

configs/vgg/README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-[BACKBONE]
+[ALGORITHM]
 
 ```latex
 @article{simonyan2014very,

docs/stat.py

@@ -1,8 +1,11 @@
 #!/usr/bin/env python
+import functools as func
 import glob
 import os.path as osp
 import re
 
+import numpy as np
+
 url_prefix = 'https://github.com/open-mmlab/mmclassification/blob/master/'
 
 files = sorted(glob.glob('../configs/*/README.md'))
@@ -18,7 +21,7 @@ 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'\[model\]\((https?.*)\)', content))
@@ -26,20 +29,34 @@ for f in files:
     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)])
+
     num_ckpts += len(ckpts)
     titles.append(title)
 
     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"""
 # Model Zoo Statistics
 
 * Number of papers: {len(set(titles))}
+{countstr}
+
 * Number of checkpoints: {num_ckpts}
 {msglist}
 """