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import os |
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import argparse |
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import torch |
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from torchvision import transforms |
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from torch.utils.data import DataLoader |
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from PIL import Image |
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from dataset.t2i_control import T2IControlCode |
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from tqdm import tqdm |
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def get_args(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--code_path', type=str, required=True, help='根目录,包含 code/control/image/caption_emb 等文件夹') |
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parser.add_argument('--code_path2', type=str, default=None, help='第二组数据路径 (可选)') |
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parser.add_argument('--image_size', type=int, default=512) |
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parser.add_argument('--downsample_size', type=int, default=8) |
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parser.add_argument('--condition_type', type=str, default='seg', choices=['seg', 'depth', 'canny', 'hed', 'lineart'], help='控制类型') |
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parser.add_argument('--get_image', action='store_true', help='是否返回 image') |
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parser.add_argument('--get_prompt', action='store_true', help='是否返回 prompt') |
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parser.add_argument('--get_label', action='store_true', help='是否返回 label') |
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parser.add_argument('--max_show', type=int, default=5, help='最多显示多少条样本') |
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return parser.parse_args() |
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def main(): |
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args = get_args() |
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dataset = T2IControlCode(args) |
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print(f"\n📦 数据集大小: {len(dataset)}") |
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loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=2, collate_fn=dataset.collate_fn) |
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for i, batch in enumerate(tqdm(loader)): |
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print(f"\n🟡 Sample #{i}") |
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print(f" - code shape: {batch['code'].shape}") |
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print(f" - control shape: {batch['control'].shape}") |
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print(f" - caption_emb shape: {batch['caption_emb'].shape}") |
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print(f" - attention mask shape: {batch['attn_mask'].shape}") |
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print(f" - valid: {batch['valid'].item()}") |
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if args.get_image: |
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print(f" - image shape: {batch['image'].shape}") |
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if args.get_prompt: |
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print(f" - prompt: {batch['prompt']}") |
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if args.get_label: |
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print(f" - label shape: {batch['label'].shape}") |
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if i + 1 >= args.max_show: |
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break |
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if __name__ == "__main__": |
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main() |
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