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())
