Add CyclicLR implementation

Slightly modified version of pull request #2016 from the pytorch repo: pytorch/pytorch#2016
The implementation follows the paper "Cyclical Learning Rates for Training Neural Networks": https://arxiv.org/abs/1506.01186

core/__init__.py

@@ -4,3 +4,4 @@
 from .predict import predict, predict_batch, predict_yield_batch
 from .evaluate import evaluate
 from .lr_finder import LRFinder
+from .cyclic_lr import CyclicLR

core/cyclic_lr.py

@@ -0,0 +1,164 @@
+import numpy as np
+from torch.optim.lr_scheduler import _LRScheduler
+from torch.optim import Optimizer
+
+
+class CyclicLR(_LRScheduler):
+    """Sets the learning rate of each parameter group according to
+    cyclical learning rate policy (CLR). The policy cycles the learning
+    rate between two boundaries with a constant frequency, as detailed in
+    the paper `Cyclical Learning Rates for Training Neural Networks`_.
+    The distance between the two boundaries can be scaled on a per-iteration
+    or per-cycle basis.
+
+    Cyclical learning rate policy changes the learning rate after every batch.
+    `step` should be called after a batch has been used for training.
+    To resume training, save `last_batch_iteration` and use it to instantiate `CycleLR`.
+
+    This class has three built-in policies, as put forth in the paper:
+    "triangular":
+        A basic triangular cycle w/ no amplitude scaling.
+    "triangular2":
+        A basic triangular cycle that scales initial amplitude by half each cycle.
+    "exp_range":
+        A cycle that scales initial amplitude by gamma**(cycle iterations) at each
+        cycle iteration.
+
+    This implementation is a slightly modified version of pytorch#2016 by thomasjpfan
+    which is adapted from the github repo `bckenstler/CLR`_.
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        max_lr (float or list): Upper boundaries in the cycle for
+            each parameter group. Functionally,
+            it defines the cycle amplitude (max_lr - base_lr).
+            Where base_lr is the learning rate defined by the
+            optimizer. The lr at any cycle is the sum of base_lr
+            and some scaling of the amplitude; therefore
+            max_lr may not actually be reached depending on
+            scaling function. Default: 0.006
+        step_size_up (int): Number of training iterations in the
+            increasing half of a cycle.
+        step_size_down (int): Number of training iterations in the
+            decreasing half of a cycle. If step_size_down is None,
+            it is set to step_size_up.
+        mode (str): One of {triangular, triangular2, exp_range}.
+            Values correspond to policies detailed above.
+            If scale_fn is not None, this argument is ignored.
+            Default: 'triangular'
+        gamma (float): Constant in 'exp_range' scaling function:
+            gamma**(cycle iterations)
+            Default: 1.0
+        scale_fn (function): Custom scaling policy defined by a single
+            argument lambda function, where
+            0 <= scale_fn(x) <= 1 for all x >= 0.
+            mode paramater is ignored
+            Default: None
+        scale_mode (str): {'cycle', 'iterations'}.
+            Defines whether scale_fn is evaluated on
+            cycle number or cycle iterations (training
+            iterations since start of cycle).
+            Default: 'cycle'
+        last_batch_idx (int): The index of the last batch. Default: -1
+
+    Example:
+        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
+        >>> scheduler = torch.optim.CyclicLR(optimizer)
+        >>> data_loader = torch.utils.data.DataLoader(...)
+        >>> for epoch in range(10):
+        >>>     for batch in data_loader:
+        >>>         scheduler.step()
+        >>>         train_batch(...)
+
+    .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
+    .. _bckenstler/CLR: https://github.com/bckenstler/CLR
+    """
+
+    def __init__(
+        self,
+        optimizer,
+        max_lr=6e-3,
+        step_size_up=2000,
+        step_size_down=None,
+        mode="triangular",
+        gamma=1.0,
+        scale_fn=None,
+        scale_mode="cycle",
+        last_batch_idx=-1,
+    ):
+
+        if not isinstance(optimizer, Optimizer):
+            raise TypeError("{} is not an Optimizer".format(type(optimizer).__name__))
+        self.optimizer = optimizer
+
+        self.max_lrs = self._format_lr("max_lr", optimizer, max_lr)
+
+        step_size_down = step_size_down or step_size_up
+        self.total_size = float(step_size_up + step_size_down)
+        self.step_ratio = float(step_size_up) / self.total_size
+
+        if mode not in ["triangular", "triangular2", "exp_range"] and scale_fn is None:
+            raise ValueError("mode is invalid and scale_fn is None")
+
+        self.mode = mode
+        self.gamma = gamma
+
+        if scale_fn is None:
+            if self.mode == "triangular":
+                self.scale_fn = self._triangular_scale_fn
+                self.scale_mode = "cycle"
+            elif self.mode == "triangular2":
+                self.scale_fn = self._triangular2_scale_fn
+                self.scale_mode = "cycle"
+            elif self.mode == "exp_range":
+                self.scale_fn = self._exp_range_scale_fn
+                self.scale_mode = "iterations"
+        else:
+            self.scale_fn = scale_fn
+            self.scale_mode = scale_mode
+        super(CyclicLR, self).__init__(optimizer, last_batch_idx)
+
+    def _format_lr(self, name, optimizer, lr):
+        """Return correctly formatted lr for each param group."""
+        if isinstance(lr, (list, tuple)):
+            if len(lr) != len(optimizer.param_groups):
+                raise ValueError(
+                    "expected {} values for {}, got {}".format(
+                        len(optimizer.param_groups), name, len(lr)
+                    )
+                )
+            return np.array(lr)
+        else:
+            return lr * np.ones(len(optimizer.param_groups))
+
+    def _triangular_scale_fn(self, x):
+        return 1.0
+
+    def _triangular2_scale_fn(self, x):
+        return 1 / (2.0 ** (x - 1))
+
+    def _exp_range_scale_fn(self, x):
+        return self.gamma ** (x)
+
+    def get_lr(self):
+        """Calculates the learning rate at batch index. This function treats
+        `self.last_epoch` as the last batch index.
+        """
+        cycle = np.floor(1 + self.last_epoch / self.total_size)
+        x = 1 + self.last_epoch / self.total_size - cycle
+        if x <= self.step_ratio:
+            scale_factor = x / self.step_ratio
+        else:
+            scale_factor = (x - 1) / (self.step_ratio - 1)
+
+        lrs = []
+        for base_lr, max_lr in zip(self.base_lrs, self.max_lrs):
+            base_height = (max_lr - base_lr) * scale_factor
+            if self.scale_mode == "cycle":
+                lr = base_lr + base_height * self.scale_fn(cycle)
+            else:
+                lr = base_lr + base_height * self.scale_fn(self.last_epoch)
+
+            lrs.append(lr)
+
+        return lrs