Added method comments

download.py

@@ -8,6 +8,7 @@
 sess = requests.Session()
 
 def parse_args():
+    """ Parse arguments """
     parser = argparse.ArgumentParser()
     parser.add_argument('--link', '-l', type=str, required=True, help='Share link of Tsinghua Cloud')
     parser.add_argument('--password', '-p', type=str, default='', help='Password of the share link')
@@ -16,6 +17,7 @@ def parse_args():
     return parser.parse_args()
 
 def get_share_key(url):
+    """ Get share key from share link """
     prefix = 'https://cloud.tsinghua.edu.cn/d/'
     if not url.startswith(prefix):
         raise ValueError('Share link of Tsinghua Cloud should start with {}'.format(prefix))
@@ -26,6 +28,7 @@ def get_share_key(url):
 
 
 def dfs_search_files(share_key: str, path="/"):
+    """ DFS search all files in the share link """
     global sess
     filelist = []
     print('https://cloud.tsinghua.edu.cn/api/v2.1/share-links/{}/dirents/?path={}'.format(share_key, path))
@@ -40,6 +43,7 @@ def dfs_search_files(share_key: str, path="/"):
     return filelist
 
 def download_single_file(url: str, fname: str):
+    """ Download single file """
     global sess
     resp = sess.get(url, stream=True)
     total = int(resp.headers.get('content-length', 0))
@@ -58,6 +62,7 @@ def download_single_file(url: str, fname: str):
             bar.update(size)
 
 def download(url, save_dir):
+    """ Download all files in the share link """
     share_key = get_share_key(url)
 
     print("Searching for files to be downloaded...")
@@ -101,6 +106,7 @@ def download(url, save_dir):
     return flag
 
 def make_data(sample):
+    """ Make data for training """
     src = ""
     for ix, ref in enumerate(sample['references']):
         src += "Reference [%d]: %s\\" % (ix+1, ref)

train_retriever.py

@@ -8,7 +8,10 @@
 from torch.utils.data.distributed import DistributedSampler
 
 class QuestionReferenceDensity(torch.nn.Module):
+    """ Question Reference Density Model """
+
     def __init__(self) -> None:
+        """ Initialize the model """
         super().__init__()
         self.question_encoder = AutoModel.from_pretrained("facebook/contriever-msmarco")
         self.reference_encoder = AutoModel.from_pretrained("facebook/contriever-msmarco")
@@ -17,12 +20,14 @@ def __init__(self) -> None:
         print("Number of parameter: %.2fM" % (total / 1e6))
 
     def mean_pooling(self, token_embeddings, mask):
+        """ Mean Pooling """
         token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)
         sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]
         return sentence_embeddings
 
 
     def forward(self, question, pos, neg):
+        """ Forward """
         global args
 
         q = self.question_encoder(**question)
@@ -41,6 +46,7 @@ def forward(self, question, pos, neg):
 
     @staticmethod
     def loss(l_pos, l_neg):
+        """ Loss """
         return torch.nn.functional.cross_entropy(torch.cat([l_pos, l_neg], dim=1), torch.arange(0, len(l_pos), dtype=torch.long, device=args.device))
 
     @staticmethod
@@ -53,6 +59,7 @@ def acc(l_pos, l_neg):
 
 
 class WarmupLinearScheduler(torch.optim.lr_scheduler.LambdaLR):
+    """ Linear warmup and then linear decay. """
     def __init__(self, optimizer, warmup, total, ratio, last_epoch=-1):
         self.warmup = warmup
         self.total = total
@@ -104,6 +111,7 @@ def save(name):
     model.reference_encoder.save_pretrained(os.path.join(log_dir, name, "reference_encoder"))
 
 def train(max_epoch = 10, eval_step = 200, save_step = 400, print_step = 50):
+    """ Train the model """
     step = 0
     for epoch in range(0, max_epoch):
         print("EPOCH %d"%epoch)

web_demo.py

@@ -39,7 +39,8 @@
 
 """
 
-def query(query: str):    
+def query(query: str):
+    """ Query the model """
 
     refs = []
     answer = "Loading ..."