TypeError: __init__() missing 1 required positional argument: 'dataset_path'

[mtk380]

when i train the script, this problem occur when running the fid_evaluation.setup_evaluation (Progress to next stage: 2%|▊ | 5000/200000 )

Traceback (most recent call last):
  File "/home/ubuntu541/anaconda3/envs/pigan/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 59, in _wrap
    fn(i, *args)
  File "/home/ubuntu541/yhj_lsp/nerf/pi-GAN-master(1)/pi-GAN-master/train.py", line 362, in train
    fid_evaluation.setup_evaluation(metadata['dataset'], generated_dir, target_size=128)
  File "/home/ubuntu541/yhj_lsp/nerf/pi-GAN-master(1)/pi-GAN-master/fid_evaluation.py", line 37, in setup_evaluation
    dataloader, CHANNELS = datasets.get_dataset(dataset_name, img_size=target_size)
  File "/home/ubuntu541/yhj_lsp/nerf/pi-GAN-master(1)/pi-GAN-master/datasets.py", line 79, in get_dataset
    dataset = globals()[name](**kwargs)
TypeError: __init__() missing 1 required positional argument: 'dataset_path'

[JinWonjoon]

Hi mtk380! I got the same error with you, and tried to fix it. The error was caused because metadata (kwargs) about dataset_path is not delivered to the dataloader.

So, in fid_evaluation.py (maybe line 32)

def setup_evaluation(dataset_name, generated_dir, data_path, target_size=128, num_imgs=8000):  # add data_path
    # Only make real images if they haven't been made yet
    real_dir = os.path.join('EvalImages', dataset_name + '_real_images_' + str(target_size))
    if not os.path.exists(real_dir):
        os.makedirs(real_dir)
        dataloader, CHANNELS = datasets.get_dataset(dataset_name, img_size=target_size, dataset_path=data_path) # add dataset_path
        print('outputting real images...')
        output_real_images(dataloader, num_imgs, real_dir)
        print('...done')

    if generated_dir is not None:
        os.makedirs(generated_dir, exist_ok=True)
    return real_dir

and, in train.py (maybe near line 366)

if opt.eval_freq > 0 and (discriminator.step + 1) % opt.eval_freq == 0:
    generated_dir = os.path.join(opt.output_dir, 'evaluation/generated')

    if rank == 0:
        fid_evaluation.setup_evaluation(metadata['dataset'], generated_dir, data_path=metadata["dataset_path"], target_size=128) # add data_path
    dist.barrier()
    ema.store(generator_ddp.parameters())
    ema.copy_to(generator_ddp.parameters())
    generator_ddp.eval()
    fid_evaluation.output_images(generator_ddp, metadata, rank, world_size, generated_dir)
    ema.restore(generator_ddp.parameters())
    dist.barrier()
    if rank == 0:
        fid = fid_evaluation.calculate_fid(metadata['dataset'], generated_dir, target_size=128)
        with open(os.path.join(opt.output_dir, f'fid.txt'), 'a') as f:
            f.write(f'\n{discriminator.step}:{fid}')

    torch.cuda.empty_cache()

In my case, it works for me.