mirror of
https://github.com/ostris/ai-toolkit.git
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658 lines
28 KiB
Python
658 lines
28 KiB
Python
import copy
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import glob
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import os
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import time
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from collections import OrderedDict
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from typing import List, Optional
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from PIL import Image
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from PIL.ImageOps import exif_transpose
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from toolkit.basic import flush
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from toolkit.models.RRDB import RRDBNet as ESRGAN, esrgan_safetensors_keys
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from safetensors.torch import save_file, load_file
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from torch.utils.data import DataLoader, ConcatDataset
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import torch
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from torch import nn
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from torchvision.transforms import transforms
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from jobs.process import BaseTrainProcess
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from toolkit.data_loader import AugmentedImageDataset
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from toolkit.esrgan_utils import convert_state_dict_to_basicsr, convert_basicsr_state_dict_to_save_format
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from toolkit.losses import ComparativeTotalVariation, get_gradient_penalty, PatternLoss
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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.style import get_style_model_and_losses
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from toolkit.train_tools import get_torch_dtype
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from diffusers import AutoencoderKL
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from tqdm import tqdm
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import time
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import numpy as np
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from .models.vgg19_critic import Critic
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IMAGE_TRANSFORMS = transforms.Compose(
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[
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transforms.ToTensor(),
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# transforms.Normalize([0.5], [0.5]),
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]
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)
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class TrainESRGANProcess(BaseTrainProcess):
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def __init__(self, process_id: int, job, config: OrderedDict):
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super().__init__(process_id, job, config)
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self.data_loader = None
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self.model: ESRGAN = None
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self.device = self.get_conf('device', self.job.device)
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self.pretrained_path = self.get_conf('pretrained_path', 'None')
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self.datasets_objects = self.get_conf('datasets', required=True)
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self.batch_size = self.get_conf('batch_size', 1, as_type=int)
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self.resolution = self.get_conf('resolution', 256, as_type=int)
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self.learning_rate = self.get_conf('learning_rate', 1e-6, as_type=float)
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self.sample_every = self.get_conf('sample_every', None)
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self.optimizer_type = self.get_conf('optimizer', 'adam')
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self.epochs = self.get_conf('epochs', None, as_type=int)
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self.max_steps = self.get_conf('max_steps', None, as_type=int)
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self.save_every = self.get_conf('save_every', None)
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self.upscale_sample = self.get_conf('upscale_sample', 4)
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self.dtype = self.get_conf('dtype', 'float32')
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self.sample_sources = self.get_conf('sample_sources', None)
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self.log_every = self.get_conf('log_every', 100, as_type=int)
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self.style_weight = self.get_conf('style_weight', 0, as_type=float)
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self.content_weight = self.get_conf('content_weight', 0, as_type=float)
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self.mse_weight = self.get_conf('mse_weight', 1e0, as_type=float)
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self.zoom = self.get_conf('zoom', 4, as_type=int)
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self.tv_weight = self.get_conf('tv_weight', 1e0, as_type=float)
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self.critic_weight = self.get_conf('critic_weight', 1, as_type=float)
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self.pattern_weight = self.get_conf('pattern_weight', 1, as_type=float)
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self.optimizer_params = self.get_conf('optimizer_params', {})
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self.augmentations = self.get_conf('augmentations', {})
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self.torch_dtype = get_torch_dtype(self.dtype)
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if self.torch_dtype == torch.bfloat16:
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self.esrgan_dtype = torch.float32
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else:
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self.esrgan_dtype = torch.float32
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self.vgg_19 = None
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self.style_weight_scalers = []
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self.content_weight_scalers = []
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# throw error if zoom if not divisible by 2
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if self.zoom % 2 != 0:
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raise ValueError('zoom must be divisible by 2')
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self.step_num = 0
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self.epoch_num = 0
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self.use_critic = self.get_conf('use_critic', False, as_type=bool)
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self.critic = None
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if self.use_critic:
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self.critic = Critic(
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device=self.device,
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dtype=self.dtype,
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process=self,
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**self.get_conf('critic', {}) # pass any other params
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)
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if self.sample_every is not None and self.sample_sources is None:
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raise ValueError('sample_every is specified but sample_sources is not')
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if self.epochs is None and self.max_steps is None:
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raise ValueError('epochs or max_steps must be specified')
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self.data_loaders = []
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# check datasets
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assert isinstance(self.datasets_objects, list)
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for dataset in self.datasets_objects:
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if 'path' not in dataset:
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raise ValueError('dataset must have a path')
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# check if is dir
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if not os.path.isdir(dataset['path']):
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raise ValueError(f"dataset path does is not a directory: {dataset['path']}")
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# make training folder
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if not os.path.exists(self.save_root):
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os.makedirs(self.save_root, exist_ok=True)
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self._pattern_loss = None
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# build augmentation transforms
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aug_transforms = []
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def update_training_metadata(self):
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self.add_meta(OrderedDict({"training_info": self.get_training_info()}))
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def get_training_info(self):
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info = OrderedDict({
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'step': self.step_num,
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'epoch': self.epoch_num,
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})
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return info
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def load_datasets(self):
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if self.data_loader is None:
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print(f"Loading datasets")
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datasets = []
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for dataset in self.datasets_objects:
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print(f" - Dataset: {dataset['path']}")
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ds = copy.copy(dataset)
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ds['resolution'] = self.resolution
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if 'augmentations' not in ds:
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ds['augmentations'] = self.augmentations
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# add the resize down augmentation
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ds['augmentations'] = [{
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'method': 'Resize',
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'params': {
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'width': int(self.resolution // self.zoom),
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'height': int(self.resolution // self.zoom),
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# downscale interpolation, string will be evaluated
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'interpolation': 'cv2.INTER_AREA'
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}
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}] + ds['augmentations']
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image_dataset = AugmentedImageDataset(ds)
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datasets.append(image_dataset)
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concatenated_dataset = ConcatDataset(datasets)
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self.data_loader = DataLoader(
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concatenated_dataset,
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batch_size=self.batch_size,
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shuffle=True,
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num_workers=6
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)
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def setup_vgg19(self):
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if self.vgg_19 is None:
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self.vgg_19, self.style_losses, self.content_losses, self.vgg19_pool_4 = get_style_model_and_losses(
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single_target=True,
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device=self.device,
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output_layer_name='pool_4',
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dtype=self.torch_dtype
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)
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self.vgg_19.to(self.device, dtype=self.torch_dtype)
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self.vgg_19.requires_grad_(False)
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# we run random noise through first to get layer scalers to normalize the loss per layer
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# bs of 2 because we run pred and target through stacked
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noise = torch.randn((2, 3, self.resolution, self.resolution), device=self.device, dtype=self.torch_dtype)
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self.vgg_19(noise)
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for style_loss in self.style_losses:
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# get a scaler to normalize to 1
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scaler = 1 / torch.mean(style_loss.loss).item()
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self.style_weight_scalers.append(scaler)
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for content_loss in self.content_losses:
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# get a scaler to normalize to 1
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scaler = 1 / torch.mean(content_loss.loss).item()
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# if is nan, set to 1
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if scaler != scaler:
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scaler = 1
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print(f"Warning: content loss scaler is nan, setting to 1")
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self.content_weight_scalers.append(scaler)
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self.print(f"Style weight scalers: {self.style_weight_scalers}")
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self.print(f"Content weight scalers: {self.content_weight_scalers}")
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def get_style_loss(self):
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if self.style_weight > 0:
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# scale all losses with loss scalers
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loss = torch.sum(
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torch.stack([loss.loss * scaler for loss, scaler in zip(self.style_losses, self.style_weight_scalers)]))
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return loss
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else:
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return torch.tensor(0.0, device=self.device)
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def get_content_loss(self):
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if self.content_weight > 0:
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# scale all losses with loss scalers
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loss = torch.sum(torch.stack(
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[loss.loss * scaler for loss, scaler in zip(self.content_losses, self.content_weight_scalers)]))
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return loss
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else:
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return torch.tensor(0.0, device=self.device)
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def get_mse_loss(self, pred, target):
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if self.mse_weight > 0:
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loss_fn = nn.MSELoss()
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loss = loss_fn(pred, target)
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return loss
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else:
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return torch.tensor(0.0, device=self.device)
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def get_tv_loss(self, pred, target):
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if self.tv_weight > 0:
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get_tv_loss = ComparativeTotalVariation()
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loss = get_tv_loss(pred, target)
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return loss
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else:
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return torch.tensor(0.0, device=self.device)
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def get_pattern_loss(self, pred, target):
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if self._pattern_loss is None:
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self._pattern_loss = PatternLoss(
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pattern_size=self.zoom,
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dtype=self.torch_dtype
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).to(self.device, dtype=self.torch_dtype)
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self._pattern_loss = self._pattern_loss.to(self.device, dtype=self.torch_dtype)
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loss = torch.mean(self._pattern_loss(pred, target))
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return loss
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def save(self, step=None):
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if not os.path.exists(self.save_root):
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os.makedirs(self.save_root, exist_ok=True)
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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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self.update_training_metadata()
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# filename = f'{self.job.name}{step_num}.safetensors'
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filename = f'{self.job.name}{step_num}.pth'
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# prepare meta
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save_meta = get_meta_for_safetensors(self.meta, self.job.name)
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# state_dict = self.model.state_dict()
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# state has the original state dict keys so we can save what we started from
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save_state_dict = self.model.state_dict()
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for key in list(save_state_dict.keys()):
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v = save_state_dict[key]
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v = v.detach().clone().to("cpu").to(torch.float32)
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save_state_dict[key] = v
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# most things wont use safetensors, save as torch
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# save_file(save_state_dict, os.path.join(self.save_root, filename), save_meta)
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torch.save(save_state_dict, os.path.join(self.save_root, filename))
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self.print(f"Saved to {os.path.join(self.save_root, filename)}")
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if self.use_critic:
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self.critic.save(step)
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def sample(self, step=None, batch: Optional[List[torch.Tensor]] = None):
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sample_folder = os.path.join(self.save_root, 'samples')
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if not os.path.exists(sample_folder):
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os.makedirs(sample_folder, exist_ok=True)
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batch_sample_folder = os.path.join(self.save_root, 'samples_batch')
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batch_targets = None
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batch_inputs = None
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if batch is not None and not os.path.exists(batch_sample_folder):
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os.makedirs(batch_sample_folder, exist_ok=True)
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self.model.eval()
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def process_and_save(img, target_img, save_path):
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img = img.to(self.device, dtype=self.esrgan_dtype)
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output = self.model(img)
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# output = (output / 2 + 0.5).clamp(0, 1)
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output = output.clamp(0, 1)
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img = img.clamp(0, 1)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
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output = output.cpu().permute(0, 2, 3, 1).squeeze(0).float().numpy()
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img = img.cpu().permute(0, 2, 3, 1).squeeze(0).float().numpy()
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# convert to pillow image
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output = Image.fromarray((output * 255).astype(np.uint8))
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img = Image.fromarray((img * 255).astype(np.uint8))
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if isinstance(target_img, torch.Tensor):
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# convert to pil
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target_img = target_img.cpu().permute(0, 2, 3, 1).squeeze(0).float().numpy()
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target_img = Image.fromarray((target_img * 255).astype(np.uint8))
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# upscale to size * self.upscale_sample while maintaining pixels
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output = output.resize(
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(self.resolution * self.upscale_sample, self.resolution * self.upscale_sample),
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resample=Image.NEAREST
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)
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img = img.resize(
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(self.resolution * self.upscale_sample, self.resolution * self.upscale_sample),
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resample=Image.NEAREST
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)
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width, height = output.size
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# stack input image and decoded image
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target_image = target_img.resize((width, height))
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output = output.resize((width, height))
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img = img.resize((width, height))
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output_img = Image.new('RGB', (width * 3, height))
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output_img.paste(img, (0, 0))
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output_img.paste(output, (width, 0))
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output_img.paste(target_image, (width * 2, 0))
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output_img.save(save_path)
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with torch.no_grad():
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for i, img_url in enumerate(self.sample_sources):
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img = exif_transpose(Image.open(img_url))
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img = img.convert('RGB')
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# crop if not square
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if img.width != img.height:
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min_dim = min(img.width, img.height)
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img = img.crop((0, 0, min_dim, min_dim))
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# resize
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img = img.resize((self.resolution * self.zoom, self.resolution * self.zoom), resample=Image.BICUBIC)
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target_image = img
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# downscale the image input
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img = img.resize((self.resolution, self.resolution), resample=Image.BICUBIC)
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# downscale the image input
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img = IMAGE_TRANSFORMS(img).unsqueeze(0).to(self.device, dtype=self.esrgan_dtype)
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img = img
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step_num = ''
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if step is not None:
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# zero-pad 9 digits
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step_num = f"_{str(step).zfill(9)}"
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seconds_since_epoch = int(time.time())
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# zero-pad 2 digits
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i_str = str(i).zfill(2)
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filename = f"{seconds_since_epoch}{step_num}_{i_str}.jpg"
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process_and_save(img, target_image, os.path.join(sample_folder, filename))
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if batch is not None:
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batch_targets = batch[0].detach()
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batch_inputs = batch[1].detach()
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batch_targets = torch.chunk(batch_targets, batch_targets.shape[0], dim=0)
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batch_inputs = torch.chunk(batch_inputs, batch_inputs.shape[0], dim=0)
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for i in range(len(batch_inputs)):
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if step is not None:
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# zero-pad 9 digits
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step_num = f"_{str(step).zfill(9)}"
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seconds_since_epoch = int(time.time())
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# zero-pad 2 digits
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i_str = str(i).zfill(2)
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filename = f"{seconds_since_epoch}{step_num}_{i_str}.jpg"
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process_and_save(batch_inputs[i], batch_targets[i], os.path.join(batch_sample_folder, filename))
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self.model.train()
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def load_model(self):
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state_dict = None
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path_to_load = self.pretrained_path
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# see if we have a checkpoint in out output to resume from
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self.print(f"Looking for latest checkpoint in {self.save_root}")
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files = glob.glob(os.path.join(self.save_root, f"{self.job.name}*.safetensors"))
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files += glob.glob(os.path.join(self.save_root, f"{self.job.name}*.pth"))
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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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# todo update step and epoch count
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elif self.pretrained_path is None:
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self.print(f" - No checkpoint found, starting from scratch")
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else:
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self.print(f" - No checkpoint found, loading pretrained model")
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self.print(f" - path: {path_to_load}")
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if path_to_load is not None:
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self.print(f" - Loading pretrained checkpoint: {path_to_load}")
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# if ends with pth then assume pytorch checkpoint
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if path_to_load.endswith('.pth') or path_to_load.endswith('.pt'):
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state_dict = torch.load(path_to_load, map_location=self.device)
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elif path_to_load.endswith('.safetensors'):
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state_dict_raw = load_file(path_to_load)
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# make ordered dict as most things need it
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state_dict = OrderedDict()
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for key in esrgan_safetensors_keys:
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state_dict[key] = state_dict_raw[key]
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else:
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raise Exception(f"Unknown file extension for checkpoint: {path_to_load}")
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# todo determine architecture from checkpoint
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self.model = ESRGAN(
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state_dict
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).to(self.device, dtype=self.esrgan_dtype)
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# set the model to training mode
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self.model.train()
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self.model.requires_grad_(True)
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def run(self):
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super().run()
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self.load_datasets()
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steps_per_step = (self.critic.num_critic_per_gen + 1)
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max_step_epochs = self.max_steps // (len(self.data_loader) // steps_per_step)
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num_epochs = self.epochs
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if num_epochs is None or num_epochs > max_step_epochs:
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num_epochs = max_step_epochs
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max_epoch_steps = len(self.data_loader) * num_epochs * steps_per_step
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num_steps = self.max_steps
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if num_steps is None or num_steps > max_epoch_steps:
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num_steps = max_epoch_steps
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self.max_steps = num_steps
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self.epochs = num_epochs
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start_step = self.step_num
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self.first_step = start_step
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self.print(f"Training ESRGAN model:")
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self.print(f" - Training folder: {self.training_folder}")
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self.print(f" - Batch size: {self.batch_size}")
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self.print(f" - Learning rate: {self.learning_rate}")
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self.print(f" - Epochs: {num_epochs}")
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self.print(f" - Max steps: {self.max_steps}")
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|
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# load model
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self.load_model()
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|
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params = self.model.parameters()
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|
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if self.style_weight > 0 or self.content_weight > 0 or self.use_critic:
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self.setup_vgg19()
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self.vgg_19.requires_grad_(False)
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self.vgg_19.eval()
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if self.use_critic:
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self.critic.setup()
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|
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optimizer = get_optimizer(params, self.optimizer_type, self.learning_rate,
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optimizer_params=self.optimizer_params)
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|
|
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# setup scheduler
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|
# todo allow other schedulers
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|
scheduler = torch.optim.lr_scheduler.ConstantLR(
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|
optimizer,
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total_iters=num_steps,
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factor=1,
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|
verbose=False
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|
)
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|
|
|
# setup tqdm progress bar
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|
self.progress_bar = tqdm(
|
|
total=num_steps,
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|
desc='Training ESRGAN',
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|
leave=True
|
|
)
|
|
|
|
blank_losses = OrderedDict({
|
|
"total": [],
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|
"style": [],
|
|
"content": [],
|
|
"mse": [],
|
|
"kl": [],
|
|
"tv": [],
|
|
"ptn": [],
|
|
"crD": [],
|
|
"crG": [],
|
|
})
|
|
epoch_losses = copy.deepcopy(blank_losses)
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|
log_losses = copy.deepcopy(blank_losses)
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|
print("Generating baseline samples")
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|
self.sample(step=0)
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|
# range start at self.epoch_num go to self.epochs
|
|
critic_losses = []
|
|
for epoch in range(self.epoch_num, self.epochs, 1):
|
|
if self.step_num >= self.max_steps:
|
|
break
|
|
flush()
|
|
for targets, inputs in self.data_loader:
|
|
if self.step_num >= self.max_steps:
|
|
break
|
|
with torch.no_grad():
|
|
is_critic_only_step = False
|
|
if self.use_critic and 1 / (self.critic.num_critic_per_gen + 1) < np.random.uniform():
|
|
is_critic_only_step = True
|
|
|
|
targets = targets.to(self.device, dtype=self.esrgan_dtype).clamp(0, 1).detach()
|
|
inputs = inputs.to(self.device, dtype=self.esrgan_dtype).clamp(0, 1).detach()
|
|
|
|
optimizer.zero_grad()
|
|
# dont do grads here for critic step
|
|
do_grad = not is_critic_only_step
|
|
with torch.set_grad_enabled(do_grad):
|
|
pred = self.model(inputs)
|
|
|
|
pred = pred.to(self.device, dtype=self.torch_dtype).clamp(0, 1)
|
|
targets = targets.to(self.device, dtype=self.torch_dtype).clamp(0, 1)
|
|
if torch.isnan(pred).any():
|
|
raise ValueError('pred has nan values')
|
|
if torch.isnan(targets).any():
|
|
raise ValueError('targets has nan values')
|
|
|
|
# Run through VGG19
|
|
if self.style_weight > 0 or self.content_weight > 0 or self.use_critic:
|
|
stacked = torch.cat([pred, targets], dim=0)
|
|
# stacked = (stacked / 2 + 0.5).clamp(0, 1)
|
|
stacked = stacked.clamp(0, 1)
|
|
self.vgg_19(stacked)
|
|
# make sure we dont have nans
|
|
if torch.isnan(self.vgg19_pool_4.tensor).any():
|
|
raise ValueError('vgg19_pool_4 has nan values')
|
|
|
|
if is_critic_only_step:
|
|
critic_d_loss = self.critic.step(self.vgg19_pool_4.tensor.detach())
|
|
critic_losses.append(critic_d_loss)
|
|
# don't do generator step
|
|
continue
|
|
else:
|
|
# doing a regular step
|
|
if len(critic_losses) == 0:
|
|
critic_d_loss = 0
|
|
else:
|
|
critic_d_loss = sum(critic_losses) / len(critic_losses)
|
|
|
|
style_loss = self.get_style_loss() * self.style_weight
|
|
content_loss = self.get_content_loss() * self.content_weight
|
|
|
|
mse_loss = self.get_mse_loss(pred, targets) * self.mse_weight
|
|
tv_loss = self.get_tv_loss(pred, targets) * self.tv_weight
|
|
pattern_loss = self.get_pattern_loss(pred, targets) * self.pattern_weight
|
|
if self.use_critic:
|
|
critic_gen_loss = self.critic.get_critic_loss(self.vgg19_pool_4.tensor) * self.critic_weight
|
|
else:
|
|
critic_gen_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype)
|
|
|
|
loss = style_loss + content_loss + mse_loss + tv_loss + critic_gen_loss + pattern_loss
|
|
# make sure non nan
|
|
if torch.isnan(loss):
|
|
raise ValueError('loss is nan')
|
|
|
|
# Backward pass and optimization
|
|
loss.backward()
|
|
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
|
|
optimizer.step()
|
|
scheduler.step()
|
|
|
|
# update progress bar
|
|
loss_value = loss.item()
|
|
# get exponent like 3.54e-4
|
|
loss_string = f"loss: {loss_value:.2e}"
|
|
if self.content_weight > 0:
|
|
loss_string += f" cnt: {content_loss.item():.2e}"
|
|
if self.style_weight > 0:
|
|
loss_string += f" sty: {style_loss.item():.2e}"
|
|
if self.mse_weight > 0:
|
|
loss_string += f" mse: {mse_loss.item():.2e}"
|
|
if self.tv_weight > 0:
|
|
loss_string += f" tv: {tv_loss.item():.2e}"
|
|
if self.pattern_weight > 0:
|
|
loss_string += f" ptn: {pattern_loss.item():.2e}"
|
|
if self.use_critic and self.critic_weight > 0:
|
|
loss_string += f" crG: {critic_gen_loss.item():.2e}"
|
|
if self.use_critic:
|
|
loss_string += f" crD: {critic_d_loss:.2e}"
|
|
|
|
if self.optimizer_type.startswith('dadaptation') or self.optimizer_type.startswith('prodigy'):
|
|
learning_rate = (
|
|
optimizer.param_groups[0]["d"] *
|
|
optimizer.param_groups[0]["lr"]
|
|
)
|
|
else:
|
|
learning_rate = optimizer.param_groups[0]['lr']
|
|
|
|
lr_critic_string = ''
|
|
if self.use_critic:
|
|
lr_critic = self.critic.get_lr()
|
|
lr_critic_string = f" lrC: {lr_critic:.1e}"
|
|
|
|
self.progress_bar.set_postfix_str(f"lr: {learning_rate:.1e}{lr_critic_string} {loss_string}")
|
|
self.progress_bar.set_description(f"E: {epoch}")
|
|
self.progress_bar.update(1)
|
|
|
|
epoch_losses["total"].append(loss_value)
|
|
epoch_losses["style"].append(style_loss.item())
|
|
epoch_losses["content"].append(content_loss.item())
|
|
epoch_losses["mse"].append(mse_loss.item())
|
|
epoch_losses["tv"].append(tv_loss.item())
|
|
epoch_losses["ptn"].append(pattern_loss.item())
|
|
epoch_losses["crG"].append(critic_gen_loss.item())
|
|
epoch_losses["crD"].append(critic_d_loss)
|
|
|
|
log_losses["total"].append(loss_value)
|
|
log_losses["style"].append(style_loss.item())
|
|
log_losses["content"].append(content_loss.item())
|
|
log_losses["mse"].append(mse_loss.item())
|
|
log_losses["tv"].append(tv_loss.item())
|
|
log_losses["ptn"].append(pattern_loss.item())
|
|
log_losses["crG"].append(critic_gen_loss.item())
|
|
log_losses["crD"].append(critic_d_loss)
|
|
|
|
# don't do on first step
|
|
if self.step_num != start_step:
|
|
if self.sample_every and self.step_num % self.sample_every == 0:
|
|
# print above the progress bar
|
|
self.print(f"Sampling at step {self.step_num}")
|
|
self.sample(self.step_num, batch=[targets, inputs])
|
|
|
|
if self.save_every and self.step_num % self.save_every == 0:
|
|
# print above the progress bar
|
|
self.print(f"Saving at step {self.step_num}")
|
|
self.save(self.step_num)
|
|
|
|
if self.log_every and self.step_num % self.log_every == 0:
|
|
# log to tensorboard
|
|
if self.writer is not None:
|
|
# get avg loss
|
|
for key in log_losses:
|
|
log_losses[key] = sum(log_losses[key]) / (len(log_losses[key]) + 1e-6)
|
|
# if log_losses[key] > 0:
|
|
self.writer.add_scalar(f"loss/{key}", log_losses[key], self.step_num)
|
|
# reset log losses
|
|
log_losses = copy.deepcopy(blank_losses)
|
|
|
|
self.step_num += 1
|
|
# end epoch
|
|
if self.writer is not None:
|
|
eps = 1e-6
|
|
# get avg loss
|
|
for key in epoch_losses:
|
|
epoch_losses[key] = sum(log_losses[key]) / (len(log_losses[key]) + eps)
|
|
if epoch_losses[key] > 0:
|
|
self.writer.add_scalar(f"epoch loss/{key}", epoch_losses[key], epoch)
|
|
# reset epoch losses
|
|
epoch_losses = copy.deepcopy(blank_losses)
|
|
|
|
self.save()
|