Create .gitignore

.gitignore

@@ -0,0 +1,139 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# VSCode
+.vscode
+
+# Other
+datasets
+checkpoints
+output_dir
+test.py
+*.pyc

util/misc.py

@@ -45,7 +45,8 @@ def synchronize_between_processes(self):
         """
         if not is_dist_avail_and_initialized():
             return
-        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
+        t = torch.tensor([self.count, self.total],
+                         dtype=torch.float64, device='cuda')
         dist.barrier()
         dist.all_reduce(t)
         t = t.tolist()
@@ -218,7 +219,8 @@ def init_distributed_mode(args):
         args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
         args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
         args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
-        args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
+        args.dist_url = "tcp://%s:%s" % (
+            os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
         os.environ['LOCAL_RANK'] = str(args.gpu)
         os.environ['RANK'] = str(args.rank)
         os.environ['WORLD_SIZE'] = str(args.world_size)
@@ -254,13 +256,16 @@ class NativeScalerWithGradNormCount:
     def __init__(self):
         self._scaler = torch.cuda.amp.GradScaler()
 
-    def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
+    def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True,
+                 error_if_nonfinite: bool = False):
         self._scaler.scale(loss).backward(create_graph=create_graph)
         if update_grad:
             if clip_grad is not None:
                 assert parameters is not None
-                self._scaler.unscale_(optimizer)  # unscale the gradients of optimizer's assigned params in-place
-                norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
+                # unscale the gradients of optimizer's assigned params in-place
+                self._scaler.unscale_(optimizer)
+                norm = torch.nn.utils.clip_grad_norm_(
+                    parameters, clip_grad, error_if_nonfinite=error_if_nonfinite)
             else:
                 self._scaler.unscale_(optimizer)
                 norm = get_grad_norm_(parameters)
@@ -286,13 +291,16 @@ def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
         return torch.tensor(0.)
     device = parameters[0].grad.device
     if norm_type == inf:
-        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
+        total_norm = max(p.grad.detach().abs().max().to(device)
+                         for p in parameters)
     else:
-        total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
+        total_norm = torch.norm(torch.stack([torch.norm(
+            p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
     return total_norm
 
 
 def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
+    #test
     output_dir = Path(args.output_dir)
     epoch_name = str(epoch)
     if loss_scaler is not None:
@@ -309,7 +317,8 @@ def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
             save_on_master(to_save, checkpoint_path)
     else:
         client_state = {'epoch': epoch}
-        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)
+        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" %
+                              epoch_name, client_state=client_state)
 
 
 def load_model(args, model_without_ddp, optimizer, loss_scaler):
@@ -337,4 +346,4 @@ def all_reduce_mean(x):
         x_reduce /= world_size
         return x_reduce.item()
     else:
-        return x
+        return x