Fix PolynomialLR power type. (#1440)

* Fix PolynomialLR power type.

torch.optim.lr_scheduler.PolynomialLR power was typed int,
but should be double.
Non-integer power is widely used for common training recipe.
E.g. torchvision's pre-trained semantic segmentation models uses
PolynomialLR as main LR scheduler with power = 0.9;
https://github.com/pytorch/vision/blob/main/references/segmentation/train.py#L201
See also https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.PolynomialLR.html

* Update RELEASENOTES.md.

---------

Co-authored-by: Masaru Kimura <masaru@hacarus.com>

Author: hiyuh

RELEASENOTES.md

@@ -7,6 +7,7 @@ __Bug Fixes__:
 
 #1426 Sequential.eval() does not put model into eval mode<br/>
 `torch.optim.lr_scheduler.LinearLR` `end_factor` default has been corrected, is now 1.0.<br/>
+`torch.optim.lr_scheduler.PolynomialLR` `power` type has been corrected, is now double.<br/>
 
 # NuGet Version 0.105.0
 

src/TorchSharp/Optimizers/LRScheduler.cs

@@ -325,7 +325,7 @@ public class PolynomialLR : LRScheduler
                         /// <param name="last_epoch">The index of last epoch. Default: -1.</param>
                         /// <param name="verbose"> If true, prints a message to stdout for each update. Default: false.</param>
                         /// <returns>A scheduler</returns>
-                        public PolynomialLR(Optimizer optimizer, int total_iters = 5, int power = 1, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)
+                        public PolynomialLR(Optimizer optimizer, int total_iters = 5, double power = 1.0, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)
                         {
                             if (optimizer == null) throw new ArgumentNullException("optimizer");
                             _power = power;
@@ -359,7 +359,7 @@ protected override IEnumerable<double> get_closed_form_lr()
                         }
 
                         private double _total_iters;
-                        private int _power;
+                        private double _power;
                     }
 
                     /// <summary>
@@ -1306,7 +1306,7 @@ public static LRScheduler MultiStepLR(Optimizer optimizer, IList<int> milestones
                 /// <param name="last_epoch">The index of last epoch. Default: -1.</param>
                 /// <param name="verbose"> If true, prints a message to stdout for each update. Default: false.</param>
                 /// <returns>A scheduler</returns>
-                public static LRScheduler PolynomialLR(Optimizer optimizer, int total_iters = 5, int power = 1, int last_epoch = -1, bool verbose = false)
+                public static LRScheduler PolynomialLR(Optimizer optimizer, int total_iters = 5, double power = 1, int last_epoch = -1, bool verbose = false)
                 {
                     return new impl.PolynomialLR(optimizer, total_iters, power, last_epoch, verbose);
                 }

test/TorchSharpTest/TestTorchTensorBugs.cs

@@ -873,7 +873,7 @@ public void ValidatePolynomialLR()
 
                 double learning_rate = 0.1;
                 var optimizer = torch.optim.SGD(seq.parameters(), learning_rate);
-                var scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, 10, 1);
+                var scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, 10, 1.0);
 
                 optimizer.zero_grad();
                 optimizer.step();
@@ -907,7 +907,7 @@ public void ValidatePolynomialLR()
 
                 double learning_rate = 0.1;
                 var optimizer = torch.optim.SGD(seq.parameters(), learning_rate);
-                var scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, 10, 2);
+                var scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, 10, 2.0);
 
                 optimizer.zero_grad();
                 optimizer.step();

test/TorchSharpTest/TestTraining.cs

@@ -1654,7 +1654,7 @@ public void TrainingSGDSequentialLRWithAllClosedFormSchedulers()
             var scheduler2 = torch.optim.lr_scheduler.StepLR(optimizer, 2);
             var scheduler3 = torch.optim.lr_scheduler.MultiStepLR(optimizer, new[] { 2, 4 });
             var scheduler4 = torch.optim.lr_scheduler.ExponentialLR(optimizer);
-            var scheduler5 = torch.optim.lr_scheduler.PolynomialLR(optimizer, power: 2);
+            var scheduler5 = torch.optim.lr_scheduler.PolynomialLR(optimizer, power: 2.0);
             var scheduler6 = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 5, 0.1);
             var scheduler7 = torch.optim.lr_scheduler.LinearLR(optimizer, end_factor: 0.75);
             var scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, new[] { scheduler0, scheduler1, scheduler2, scheduler3, scheduler4, scheduler5, scheduler6, scheduler7}, new[] { 5, 5, 5, 5, 5, 5, 5 });