From c66ca7420560e9ab0e8893f6c7d6a496f128c79a Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 3 Sep 2023 01:13:49 +0200
Subject: Delete laprop

---
 text_recognizer/optimizer/__init__.py |   0
 text_recognizer/optimizer/laprop.py   | 129 ----------------------------------
 2 files changed, 129 deletions(-)
 delete mode 100644 text_recognizer/optimizer/__init__.py
 delete mode 100644 text_recognizer/optimizer/laprop.py

diff --git a/text_recognizer/optimizer/__init__.py b/text_recognizer/optimizer/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/text_recognizer/optimizer/laprop.py b/text_recognizer/optimizer/laprop.py
deleted file mode 100644
index 9b4f4d4..0000000
--- a/text_recognizer/optimizer/laprop.py
+++ /dev/null
@@ -1,129 +0,0 @@
-"""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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