0.9.17

Author: divideconcept

torchstudio/optim/adam.py

@@ -20,7 +20,7 @@ class Adam(optim.Adam):
             algorithm from the paper `On the Convergence of Adam and Beyond`_
             (default: False)
 
-    .. _Adam\: A Method for Stochastic Optimization:
+    .. _Adam: A Method for Stochastic Optimization:
         https://arxiv.org/abs/1412.6980
     .. _Decoupled Weight Decay Regularization:
         https://arxiv.org/abs/1711.05101

torchstudio/pythoninstall.py

@@ -3,6 +3,7 @@
 import importlib.util
 import argparse
 import subprocess
+import requests
 parser = argparse.ArgumentParser()
 parser.add_argument("--cuda", help="install nvidia gpu support", action="store_true", default=False)
 parser.add_argument("--package", help="install specific package", action='append', nargs='+', default=[])
@@ -30,7 +31,6 @@
         if args.cuda:
             print("Checking the latest supported CUDA version...")
             highest_cuda_version=118 #11.8 highest supported cuda version for PyTorch 2.0
-            import requests
             try:
                 pytorch_repo = requests.get("https://download.pytorch.org/whl/torch")
             except:

torchstudio/pythonparse.py

@@ -148,7 +148,7 @@ def filter_parent_objects(objects:List[Dict]) -> List[Dict]:
         for subobject in objects:
             name=object['name']
             if subobject['name']!=name:
-                if re.search('[ =+]'+name+'[ ]*\(', subobject['code']):
+                if re.search(r'[ =+]'+name+r'[ ]*\(', subobject['code']):
                     unique=False
         if unique:
             parent_objects.append(object)

torchstudio/schedulers/onecycle.py

@@ -95,7 +95,7 @@ class OneCycle(lr_scheduler.OneCycleLR):
         >>>         scheduler.step()
 
 
-    .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
+    .. _Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates:
         https://arxiv.org/abs/1708.07120
     """
     def __init__(self,

torchstudio/sshtunnel.py

@@ -268,13 +268,13 @@ def finish(self):
             print("Cleaning TorchStudio cache...", file=sys.stderr)
             stdin, stdout, stderr = sshclient.exec_command('rm -r -f TorchStudio/cache')
             exit_status = stdout.channel.recv_exit_status()
-            stdin, stdout, stderr = sshclient.exec_command('rmdir /s /q TorchStudio\cache')
+            stdin, stdout, stderr = sshclient.exec_command('rmdir /s /q TorchStudio\\cache')
             exit_status = stdout.channel.recv_exit_status()
         if args.clean==1:
             print("Deleting TorchStudio environment...", file=sys.stderr)
             stdin, stdout, stderr = sshclient.exec_command('rm -r -f TorchStudio/python')
             exit_status = stdout.channel.recv_exit_status()
-            stdin, stdout, stderr = sshclient.exec_command('rmdir /s /q TorchStudio\python')
+            stdin, stdout, stderr = sshclient.exec_command('rmdir /s /q TorchStudio\\python')
             exit_status = stdout.channel.recv_exit_status()
         if args.clean==2:
             print("Deleting all TorchStudio files...", file=sys.stderr)