mirror of
https://github.com/ostris/ai-toolkit.git
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221 lines
7.8 KiB
Python
221 lines
7.8 KiB
Python
import glob
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import os
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import numpy as np
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import torch
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import torch.nn as nn
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from safetensors.torch import load_file, save_file
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from toolkit.losses import get_gradient_penalty
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from toolkit.metadata import get_meta_for_safetensors
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from toolkit.optimizer import get_optimizer
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from toolkit.train_tools import get_torch_dtype
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from typing import TYPE_CHECKING, Union
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class MeanReduce(nn.Module):
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def __init__(self):
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super(MeanReduce, self).__init__()
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def forward(self, inputs):
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return torch.mean(inputs, dim=(1, 2, 3), keepdim=True)
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class Vgg19Critic(nn.Module):
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def __init__(self):
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# vgg19 input (bs, 3, 512, 512)
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# pool1 (bs, 64, 256, 256)
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# pool2 (bs, 128, 128, 128)
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# pool3 (bs, 256, 64, 64)
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# pool4 (bs, 512, 32, 32) <- take this input
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super(Vgg19Critic, self).__init__()
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self.main = nn.Sequential(
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# input (bs, 512, 32, 32)
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# nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1),
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nn.utils.spectral_norm( # SN keeps D’s scale in check
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nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1)
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),
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nn.LeakyReLU(0.2), # (bs, 512, 16, 16)
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# nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1),
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nn.utils.spectral_norm(
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nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1)
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),
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nn.LeakyReLU(0.2), # (bs, 512, 8, 8)
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# nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1),
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nn.utils.spectral_norm(
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nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1)
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),
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# (bs, 1, 4, 4)
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MeanReduce(), # (bs, 1, 1, 1)
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nn.Flatten(), # (bs, 1)
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# nn.Flatten(), # (128*8*8) = 8192
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# nn.Linear(128 * 8 * 8, 1)
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)
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def forward(self, inputs):
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# return self.main(inputs)
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with torch.cuda.amp.autocast(False):
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return self.main(inputs.float())
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if TYPE_CHECKING:
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from jobs.process.TrainVAEProcess import TrainVAEProcess
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from jobs.process.TrainESRGANProcess import TrainESRGANProcess
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class Critic:
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process: Union['TrainVAEProcess', 'TrainESRGANProcess']
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def __init__(
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self,
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learning_rate=1e-5,
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device='cpu',
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optimizer='adam',
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num_critic_per_gen=1,
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dtype='float32',
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lambda_gp=10,
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start_step=0,
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warmup_steps=1000,
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process=None,
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optimizer_params=None,
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):
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self.learning_rate = learning_rate
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self.device = device
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self.optimizer_type = optimizer
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self.num_critic_per_gen = num_critic_per_gen
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self.dtype = dtype
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self.torch_dtype = get_torch_dtype(self.dtype)
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self.process = process
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self.model = None
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self.optimizer = None
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self.scheduler = None
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self.warmup_steps = warmup_steps
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self.start_step = start_step
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self.lambda_gp = lambda_gp
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if optimizer_params is None:
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optimizer_params = {}
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self.optimizer_params = optimizer_params
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self.print = self.process.print
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print(f" Critic config: {self.__dict__}")
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def setup(self):
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self.model = Vgg19Critic().to(self.device)
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self.load_weights()
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self.model.train()
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self.model.requires_grad_(True)
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params = self.model.parameters()
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self.optimizer = get_optimizer(params, self.optimizer_type, self.learning_rate,
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optimizer_params=self.optimizer_params)
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self.scheduler = torch.optim.lr_scheduler.ConstantLR(
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self.optimizer,
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total_iters=self.process.max_steps * self.num_critic_per_gen,
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factor=1,
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verbose=False
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)
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def load_weights(self):
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path_to_load = None
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self.print(f"Critic: Looking for latest checkpoint in {self.process.save_root}")
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files = glob.glob(os.path.join(self.process.save_root, f"CRITIC_{self.process.job.name}*.safetensors"))
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if files and len(files) > 0:
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latest_file = max(files, key=os.path.getmtime)
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print(f" - Latest checkpoint is: {latest_file}")
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path_to_load = latest_file
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else:
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self.print(f" - No checkpoint found, starting from scratch")
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if path_to_load:
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self.model.load_state_dict(load_file(path_to_load))
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def save(self, step=None):
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self.process.update_training_metadata()
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save_meta = get_meta_for_safetensors(self.process.meta, self.process.job.name)
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step_num = ''
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if step is not None:
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# zeropad 9 digits
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step_num = f"_{str(step).zfill(9)}"
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save_path = os.path.join(self.process.save_root, f"CRITIC_{self.process.job.name}{step_num}.safetensors")
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save_file(self.model.state_dict(), save_path, save_meta)
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self.print(f"Saved critic to {save_path}")
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def get_critic_loss(self, vgg_output):
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if self.start_step > self.process.step_num:
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return torch.tensor(0.0, dtype=self.torch_dtype, device=self.device)
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warmup_scaler = 1.0
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# we need a warmup when we come on of 1000 steps
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# we want to scale the loss by 0.0 at self.start_step steps and 1.0 at self.start_step + warmup_steps
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if self.process.step_num < self.start_step + self.warmup_steps:
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warmup_scaler = (self.process.step_num - self.start_step) / self.warmup_steps
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# set model to not train for generator loss
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self.model.eval()
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self.model.requires_grad_(False)
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# vgg_pred, vgg_target = torch.chunk(vgg_output, 2, dim=0)
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vgg_pred, vgg_target = torch.chunk(vgg_output.float(), 2, dim=0)
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# run model
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stacked_output = self.model(vgg_pred)
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return (-torch.mean(stacked_output)) * warmup_scaler
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def step(self, vgg_output):
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# train critic here
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self.model.train()
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self.model.requires_grad_(True)
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self.optimizer.zero_grad()
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critic_losses = []
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# inputs = vgg_output.detach()
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# inputs = inputs.to(self.device, dtype=self.torch_dtype)
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inputs = vgg_output.detach().to(self.device, dtype=torch.float32)
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self.optimizer.zero_grad()
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vgg_pred, vgg_target = torch.chunk(inputs, 2, dim=0)
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# stacked_output = self.model(inputs).float()
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# out_pred, out_target = torch.chunk(stacked_output, 2, dim=0)
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# # Compute gradient penalty
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# gradient_penalty = get_gradient_penalty(self.model, vgg_target, vgg_pred, self.device)
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# # Compute WGAN-GP critic loss
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# critic_loss = -(torch.mean(out_target) - torch.mean(out_pred)) + self.lambda_gp * gradient_penalty
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stacked_output = self.model(inputs).float()
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out_pred, out_target = torch.chunk(stacked_output, 2, dim=0)
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# ── hinge loss ──
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loss_real = torch.relu(1.0 - out_target).mean()
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loss_fake = torch.relu(1.0 + out_pred).mean()
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# gradient penalty (unchanged helper)
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gradient_penalty = get_gradient_penalty(self.model, vgg_target, vgg_pred, self.device)
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critic_loss = loss_real + loss_fake + self.lambda_gp * gradient_penalty
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critic_loss.backward()
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torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
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self.optimizer.step()
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self.scheduler.step()
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critic_losses.append(critic_loss.item())
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# avg loss
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loss = np.mean(critic_losses)
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return loss
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def get_lr(self):
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if self.optimizer_type.startswith('dadaptation'):
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learning_rate = (
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self.optimizer.param_groups[0]["d"] *
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self.optimizer.param_groups[0]["lr"]
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)
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else:
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learning_rate = self.optimizer.param_groups[0]['lr']
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return learning_rate
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