def test_dataset_bug(): from hub import Dataset, schema Dataset( "./data/test/test_dataset_bug", shape=(4,), mode="w", schema={ "image": schema.Tensor((512, 512), dtype="float"), "label": schema.Tensor((512, 512), dtype="float"), }, ) was_except = False try: Dataset("./data/test/test_dataset_bug", mode="w") except Exception: was_except = True assert was_except Dataset( "./data/test/test_dataset_bug", shape=(4,), mode="w", schema={ "image": schema.Tensor((512, 512), dtype="float"), "label": schema.Tensor((512, 512), dtype="float"), }, )
def main(): # Create dataset ds = Dataset( "davitb/pytorch_example", shape=(640, ), mode="w", schema={ "image": schema.Tensor((512, 512), dtype="float"), "label": schema.Tensor((512, 512), dtype="float"), }, ) # ds["image"][:] = 1 # ds["label"][:] = 2 # Load to pytorch ds = ds.to_pytorch() ds = torch.utils.data.DataLoader( ds, batch_size=8, num_workers=2, ) # Iterate for batch in ds: print(batch["image"], batch["label"])
def main(): # Create dataset ds = Dataset( "./data/example/pytorch", shape=(64, ), schema={ "image": schema.Tensor((512, 512), dtype="float"), "label": schema.Tensor((512, 512), dtype="float"), }, ) # tansform into Tensorflow dataset ds = ds.to_tensorflow().batch(8) # Iterate over the data for batch in ds: print(batch["image"], batch["label"])
def main(): # Tag is set {Username}/{Dataset} tag = "davitb/basic11" # Create dataset ds = Dataset( tag, shape=(4, ), schema={ "image": schema.Tensor((512, 512), dtype="float"), "label": schema.Tensor((512, 512), dtype="float"), }, ) # Upload Data ds["image"][:] = np.ones((4, 512, 512)) ds["label"][:] = np.ones((4, 512, 512)) ds.commit() # Load the data ds = Dataset(tag) print(ds["image"][0].compute())