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import os
from typing import List
import torch
from omegaconf import OmegaConf
import numpy as np
from sdata import create_dataset, create_loader
num_it = 100
max_vis = 3
if __name__ == "__main__":
filedir = os.path.realpath(os.path.dirname(__file__))
config_path = os.path.join(filedir, "configs", "example.yaml")
config = OmegaConf.load(config_path)
# build config
datapipeline = create_dataset(**config.dataset)
# build loader
loader = create_loader(datapipeline, **config.loader)
print(f"Yielding {num_it} batches")
for i, batch in enumerate(loader):
if i >= num_it:
break
for key in batch:
if isinstance(batch[key], (torch.Tensor, np.ndarray)):
print(key, batch[key].shape)
elif isinstance(batch[key], (List)):
print(key)
print(batch[key][:max_vis])
print("ciao")
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