tensordict_checks.md

file_format	mystnb

(pytorch-checks)=

Checks and Lazy Validation

Use {class}~pandera.api.checks.Check to validate tensor values.

Apply checks to Tensor components

import torch
from tensordict import TensorDict
from pandera import Check
import pandera.tensordict as pa

schema = pa.TensorDictSchema(
    keys={
        "values": pa.Tensor(
            dtype=torch.float32,
            shape=(None,),
            checks=[Check.greater_than(0.0), Check.less_than(1.0)],
        ),
    },
    batch_size=(10,),
)

td = TensorDict(
    {"values": torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95])},
    batch_size=[10],
)
validated = schema.validate(td)

Lazy validation

Set lazy=True to collect all errors:

td_invalid = TensorDict(
    {"values": torch.tensor([-0.1, 0.2, 0.3, 10.0, 0.5])},
    batch_size=[5],
)

try:
    schema.validate(td_invalid, lazy=True)
except pa.errors.SchemaErrors as exc:
    print(f"Found {len(exc.schema_errors)} errors")
    print(exc)

Available checks

Standard pandera checks work with tensor data:

• {func}~pandera.api.checks.Check.greater_than
• {func}~pandera.api.checks.Check.less_than
• {func}~pandera.api.checks.Check.greater_than_or_equal_to
• {func}~pandera.api.checks.Check.less_than_or_equal_to
• {func}~pandera.api.checks.Check.in_range
• {func}~pandera.api.checks.Check.isin
• {func}~pandera.api.checks.Check.notin
• {func}~pandera.api.checks.Check.str_matches
• {func}~pandera.api.checks.Check.str_contains
• {func}~pandera.api.checks.Check.str_startswith
• {func}~pandera.api.checks.Check.str_endswith

See also

• {ref}checks — full Check API reference
• {ref}pytorch-error-reporting — error handling