From cf558e7146eabdf1e2c3435af31f4e87f4eb18bd Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 29 Jan 2023 21:16:40 +0100
Subject: Add laprop

---
 text_recognizer/optimizers/__init__.py |   0
 text_recognizer/optimizers/laprop.py   | 129 +++++++++++++++++++++++++++++++++
 2 files changed, 129 insertions(+)
 create mode 100644 text_recognizer/optimizers/__init__.py
 create mode 100644 text_recognizer/optimizers/laprop.py

diff --git a/text_recognizer/optimizers/__init__.py b/text_recognizer/optimizers/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/text_recognizer/optimizers/laprop.py b/text_recognizer/optimizers/laprop.py
new file mode 100644
index 0000000..9b4f4d4
--- /dev/null
+++ b/text_recognizer/optimizers/laprop.py
@@ -0,0 +1,129 @@
+"""https://github.com/Z-T-WANG/LaProp-Optimizer/blob/master/laprop.py"""
+from torch.optim import Optimizer
+import math
+import torch
+
+
+class LaProp(Optimizer):
+    def __init__(
+        self,
+        params,
+        lr=4e-4,
+        betas=(0.9, 0.999),
+        eps=1e-15,
+        weight_decay=0,
+        amsgrad=False,
+        centered=False,
+    ):
+
+        self.steps_before_using_centered = 10
+
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            amsgrad=amsgrad,
+            centered=centered,
+        )
+        super(LaProp, self).__init__(params, defaults)
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        "Adam does not support sparse gradients, please consider SparseAdam instead"
+                    )
+                amsgrad = group["amsgrad"]
+                centered = group["centered"]
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state["step"] = 0
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(p.data)
+                    # Exponential moving average of learning rates
+                    state["exp_avg_lr_1"] = 0.0
+                    state["exp_avg_lr_2"] = 0.0
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(p.data)
+                    if centered:
+                        # Exponential moving average of gradient values as calculated by beta2
+                        state["exp_mean_avg_beta2"] = torch.zeros_like(p.data)
+                    if amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state["max_exp_avg_sq"] = torch.zeros_like(p.data)
+
+                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+                if centered:
+                    exp_mean_avg_beta2 = state["exp_mean_avg_beta2"]
+                if amsgrad:
+                    max_exp_avg_sq = state["max_exp_avg_sq"]
+                beta1, beta2 = group["betas"]
+
+                state["step"] += 1
+
+                # Decay the first and second moment running average coefficient
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+
+                state["exp_avg_lr_1"] = (
+                    state["exp_avg_lr_1"] * beta1 + (1 - beta1) * group["lr"]
+                )
+                state["exp_avg_lr_2"] = state["exp_avg_lr_2"] * beta2 + (1 - beta2)
+
+                bias_correction1 = (
+                    state["exp_avg_lr_1"] / group["lr"] if group["lr"] != 0.0 else 1.0
+                )  # 1 - beta1 ** state['step']
+                step_size = 1 / bias_correction1
+
+                bias_correction2 = state["exp_avg_lr_2"]
+
+                denom = exp_avg_sq
+                if centered:
+                    exp_mean_avg_beta2.mul_(beta2).add_(1 - beta2, grad)
+                    if state["step"] > self.steps_before_using_centered:
+                        mean = exp_mean_avg_beta2**2
+                        denom = denom - mean
+
+                if amsgrad:
+                    if not (
+                        centered and state["step"] <= self.steps_before_using_centered
+                    ):
+                        # Maintains the maximum of all (centered) 2nd moment running avg. till now
+                        torch.max(max_exp_avg_sq, denom, out=max_exp_avg_sq)
+                        # Use the max. for normalizing running avg. of gradient
+                        denom = max_exp_avg_sq
+
+                denom = denom.div(bias_correction2).sqrt_().add_(group["eps"])
+                step_of_this_grad = grad / denom
+                exp_avg.mul_(beta1).add_((1 - beta1) * group["lr"], step_of_this_grad)
+
+                p.data.add_(-step_size, exp_avg)
+                if group["weight_decay"] != 0:
+                    p.data.add_(-group["weight_decay"], p.data)
+
+        return loss
-- 
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