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111 lines
2.8 KiB
Python
111 lines
2.8 KiB
Python
import time
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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from torchvision import transforms
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import sys
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import os
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import cv2
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from toolkit.paths import SD_SCRIPTS_ROOT
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from toolkit.image_utils import show_img
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sys.path.append(SD_SCRIPTS_ROOT)
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from library.model_util import load_vae
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from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
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from toolkit.data_loader import AiToolkitDataset, get_dataloader_from_datasets, \
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trigger_dataloader_setup_epoch
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from toolkit.config_modules import DatasetConfig
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import argparse
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from tqdm import tqdm
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parser = argparse.ArgumentParser()
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parser.add_argument('dataset_folder', type=str, default='input')
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parser.add_argument('--epochs', type=int, default=1)
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args = parser.parse_args()
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dataset_folder = args.dataset_folder
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resolution = 1024
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bucket_tolerance = 64
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batch_size = 1
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##
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dataset_config = DatasetConfig(
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dataset_path=dataset_folder,
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resolution=resolution,
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# caption_ext='json',
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default_caption='default',
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# clip_image_path='/mnt/Datasets2/regs/yetibear_xl_v14/random_aspect/',
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buckets=True,
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bucket_tolerance=bucket_tolerance,
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# poi='person',
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# augmentations=[
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# {
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# 'method': 'RandomBrightnessContrast',
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# 'brightness_limit': (-0.3, 0.3),
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# 'contrast_limit': (-0.3, 0.3),
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# 'brightness_by_max': False,
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# 'p': 1.0
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# },
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# {
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# 'method': 'HueSaturationValue',
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# 'hue_shift_limit': (-0, 0),
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# 'sat_shift_limit': (-40, 40),
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# 'val_shift_limit': (-40, 40),
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# 'p': 1.0
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# },
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# {
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# 'method': 'RGBShift',
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# 'r_shift_limit': (-20, 20),
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# 'g_shift_limit': (-20, 20),
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# 'b_shift_limit': (-20, 20),
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# 'p': 1.0
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# },
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# ]
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)
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dataloader: DataLoader = get_dataloader_from_datasets([dataset_config], batch_size=batch_size)
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# run through an epoch ang check sizes
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dataloader_iterator = iter(dataloader)
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for epoch in range(args.epochs):
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for batch in tqdm(dataloader):
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batch: 'DataLoaderBatchDTO'
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img_batch = batch.tensor
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chunks = torch.chunk(img_batch, batch_size, dim=0)
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# put them so they are size by side
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big_img = torch.cat(chunks, dim=3)
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big_img = big_img.squeeze(0)
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min_val = big_img.min()
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max_val = big_img.max()
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big_img = (big_img / 2 + 0.5).clamp(0, 1)
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# convert to image
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img = transforms.ToPILImage()(big_img)
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show_img(img)
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# time.sleep(0.1)
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# if not last epoch
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if epoch < args.epochs - 1:
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trigger_dataloader_setup_epoch(dataloader)
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cv2.destroyAllWindows()
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print('done')
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