Typings added

mindee · Dec 9, 2023 · 22e323c · 22e323c
1 parent fff69d4
commit 22e323c
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Showing 12 changed files with 19 additions and 12 deletions.
diff --git a/references/classification/train_pytorch.py b/references/classification/train_pytorch.py
@@ -331,7 +331,7 @@ def main(args):
     min_loss = np.inf
     # Training loop
     if args.early_stop:
-        early_stopper = EarlyStopper(patience=args.early_stop_epochs)
+        early_stopper = EarlyStopper(patience=args.early_stop_epochs, min_delta=args.early_stop_delta)
     for epoch in range(args.epochs):
         fit_one_epoch(model, train_loader, batch_transforms, optimizer, scheduler)
 
@@ -421,6 +421,7 @@ def parse_args():
     parser.add_argument("--find-lr", action="store_true", help="Gridsearch the optimal LR")
     parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
     parser.add_argument("--early-stop-epochs", type=int, default=5, help="Patience for early stopping")
+    parser.add_argument("--early-stop-delta", type=float, default=0.01, help="Minimum Delta for early stopping")
     args = parser.parse_args()
 
     return args

diff --git a/references/classification/train_tensorflow.py b/references/classification/train_tensorflow.py
@@ -295,7 +295,7 @@ def main(args):
 
     # Training loop
     if args.early_stop:
-        early_stopper = EarlyStopper(patience=args.early_stop_epochs)
+        early_stopper = EarlyStopper(patience=args.early_stop_epochs, min_delta=args.early_stop_delta)
     for epoch in range(args.epochs):
         fit_one_epoch(model, train_loader, batch_transforms, optimizer, args.amp)
 
@@ -395,6 +395,7 @@ def parse_args():
     parser.add_argument("--find-lr", action="store_true", help="Gridsearch the optimal LR")
     parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
     parser.add_argument("--early-stop-epochs", type=int, default=5, help="Patience for early stopping")
+    parser.add_argument("--early-stop-delta", type=float, default=0.01, help="Minimum Delta for early stopping")
     args = parser.parse_args()
 
     return args

diff --git a/references/classification/utils.py b/references/classification/utils.py
@@ -81,7 +81,7 @@ def __init__(self, patience: int = 5, min_delta: float = 0.01):
         self.counter = 0
         self.min_validation_loss = float("inf")
 
-    def early_stop(self, validation_loss: float):
+    def early_stop(self, validation_loss: float) -> bool:
         if validation_loss < self.min_validation_loss:
             self.min_validation_loss = validation_loss
             self.counter = 0

diff --git a/references/detection/train_pytorch.py b/references/detection/train_pytorch.py
@@ -368,7 +368,7 @@ def main(args):
     # Create loss queue
     min_loss = np.inf
     if args.early_stop:
-        early_stopper = EarlyStopper(patience=args.early_stop_epochs)
+        early_stopper = EarlyStopper(patience=args.early_stop_epochs, min_delta=args.early_stop_delta)
 
     # Training loop
     for epoch in range(args.epochs):
@@ -451,6 +451,7 @@ def parse_args():
     parser.add_argument("--find-lr", action="store_true", help="Gridsearch the optimal LR")
     parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
     parser.add_argument("--early-stop-epochs", type=int, default=5, help="Patience for early stopping")
+    parser.add_argument("--early-stop-delta", type=float, default=0.01, help="Minimum Delta for early stopping")
     args = parser.parse_args()
 
     return args

diff --git a/references/detection/train_tensorflow.py b/references/detection/train_tensorflow.py
@@ -327,7 +327,7 @@ def main(args):
 
     min_loss = np.inf
     if args.early_stop:
-        early_stopper = EarlyStopper(patience=args.early_stop_epochs)
+        early_stopper = EarlyStopper(patience=args.early_stop_epochs, min_delta=args.early_stop_delta)
 
     # Training loop
     for epoch in range(args.epochs):
@@ -419,6 +419,7 @@ def parse_args():
     parser.add_argument("--find-lr", action="store_true", help="Gridsearch the optimal LR")
     parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
     parser.add_argument("--early-stop-epochs", type=int, default=5, help="Patience for early stopping")
+    parser.add_argument("--early-stop-delta", type=float, default=0.01, help="Minimum Delta for early stopping")
     args = parser.parse_args()
 
     return args

diff --git a/references/detection/utils.py b/references/detection/utils.py
@@ -100,7 +100,7 @@ def __init__(self, patience: int = 5, min_delta: float = 0.01):
         self.counter = 0
         self.min_validation_loss = float("inf")
 
-    def early_stop(self, validation_loss: float):
+    def early_stop(self, validation_loss: float) -> bool:
         if validation_loss < self.min_validation_loss:
             self.min_validation_loss = validation_loss
             self.counter = 0

diff --git a/references/obj_detection/train_pytorch.py b/references/obj_detection/train_pytorch.py
@@ -306,7 +306,7 @@ def main(args):
 
     max_score = 0.0
     if args.early_stop:
-        early_stopper = EarlyStopper(patience=args.early_stop_epochs)
+        early_stopper = EarlyStopper(patience=args.early_stop_epochs, min_delta=args.early_stop_delta)
     for epoch in range(args.epochs):
         fit_one_epoch(model, train_loader, optimizer, scheduler, amp=args.amp)
         # Validation loop at the end of each epoch

diff --git a/references/obj_detection/utils.py b/references/obj_detection/utils.py
@@ -86,7 +86,7 @@ def __init__(self, patience: int = 5, min_delta: float = 0.01):
         self.counter = 0
         self.min_validation_loss = float("inf")
 
-    def early_stop(self, validation_loss: float):
+    def early_stop(self, validation_loss: float) -> bool:
         if validation_loss < self.min_validation_loss:
             self.min_validation_loss = validation_loss
             self.counter = 0

diff --git a/references/recognition/train_pytorch.py b/references/recognition/train_pytorch.py
@@ -393,7 +393,7 @@ def main(args):
     min_loss = np.inf
     # Training loop
     if args.early_stop:
-        early_stopper = EarlyStopper(patience=args.early_stop_epochs)
+        early_stopper = EarlyStopper(patience=args.early_stop_epochs, min_delta=args.early_stop_delta)
     for epoch in range(args.epochs):
         fit_one_epoch(model, train_loader, batch_transforms, optimizer, scheduler, amp=args.amp)
 
@@ -484,6 +484,7 @@ def parse_args():
     parser.add_argument("--find-lr", action="store_true", help="Gridsearch the optimal LR")
     parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
     parser.add_argument("--early-stop-epochs", type=int, default=5, help="Patience for early stopping")
+    parser.add_argument("--early-stop-delta", type=float, default=0.01, help="Minimum Delta for early stopping")
     args = parser.parse_args()
 
     return args

diff --git a/references/recognition/train_pytorch_ddp.py b/references/recognition/train_pytorch_ddp.py
@@ -322,7 +322,7 @@ def main(rank: int, world_size: int, args):
     # Create loss queue
     min_loss = np.inf
     if args.early_stop:
-        early_stopper = EarlyStopper(patience=args.early_stop_epochs)
+        early_stopper = EarlyStopper(patience=args.early_stop_epochs, min_delta=args.early_stop_delta)
     # Training loop
     for epoch in range(args.epochs):
         fit_one_epoch(model, device, train_loader, batch_transforms, optimizer, scheduler, amp=args.amp)
@@ -421,6 +421,7 @@ def parse_args():
     parser.add_argument("--amp", dest="amp", help="Use Automatic Mixed Precision", action="store_true")
     parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
     parser.add_argument("--early-stop-epochs", type=int, default=5, help="Patience for early stopping")
+    parser.add_argument("--early-stop-delta", type=float, default=0.01, help="Minimum Delta for early stopping")
     args = parser.parse_args()
 
     return args

diff --git a/references/recognition/train_tensorflow.py b/references/recognition/train_tensorflow.py
@@ -348,7 +348,7 @@ def main(args):
 
     min_loss = np.inf
     if args.early_stop:
-        early_stopper = EarlyStopper(patience=args.early_stop_epochs)
+        early_stopper = EarlyStopper(patience=args.early_stop_epochs, min_delta=args.early_stop_delta)
     # Training loop
     for epoch in range(args.epochs):
         fit_one_epoch(model, train_loader, batch_transforms, optimizer, args.amp)
@@ -447,6 +447,7 @@ def parse_args():
     parser.add_argument("--find-lr", action="store_true", help="Gridsearch the optimal LR")
     parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
     parser.add_argument("--early-stop-epochs", type=int, default=5, help="Patience for early stopping")
+    parser.add_argument("--early-stop-delta", type=float, default=0.01, help="Minimum Delta for early stopping")
     args = parser.parse_args()
 
     return args

diff --git a/references/recognition/utils.py b/references/recognition/utils.py
@@ -81,7 +81,7 @@ def __init__(self, patience: int = 5, min_delta: float = 0.01):
         self.counter = 0
         self.min_validation_loss = float("inf")
 
-    def early_stop(self, validation_loss: float):
+    def early_stop(self, validation_loss: float) -> bool:
         if validation_loss < self.min_validation_loss:
             self.min_validation_loss = validation_loss
             self.counter = 0