mirror of
https://github.com/ostris/ai-toolkit.git
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71 lines
1.6 KiB
Python
71 lines
1.6 KiB
Python
import time
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import numpy as np
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import torch
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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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from toolkit.config_modules import DatasetConfig
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('dataset_folder', type=str, default='input')
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args = parser.parse_args()
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dataset_folder = args.dataset_folder
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resolution = 512
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bucket_tolerance = 64
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batch_size = 4
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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='txt',
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default_caption='default',
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buckets=True,
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bucket_tolerance=bucket_tolerance,
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)
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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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for batch in 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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#
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# min_val = big_img.min()
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# max_val = big_img.max()
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#
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# big_img = (big_img / 2 + 0.5).clamp(0, 1)
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#
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# # convert to image
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# img = transforms.ToPILImage()(big_img)
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#
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# show_img(img)
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#
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# time.sleep(1.0)
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cv2.destroyAllWindows()
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print('done')
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