Use Pydantic to build complex types from parameter specifications.

Installation

• rye add cast --git https://github.com/flywhl/cast

Usage

import cast
from cast import Cast, CastModel
from torch import Tensor
import torch
import yaml

# 1. Create and register some useful parameterisations
#       (or soon install from PyPi, i.e. `rye add cast-torch`)

@cast.for_type(Tensor)
class NormalTensor(Cast[Tensor]):

    mean: float
    std: float
    size: tuple[int, ...]

    def build(self) -> Tensor:
        return torch.normal(self.mean, self.std, size=self.size)

@cast.for_type(Tensor)
class UniformTensor(Cast[Tensor]):
    low: float
    high: float
    size: tuple[int, ...]

    def build(self) -> Tensor:
      return torch.empty(self.size).uniform_(self.low, self.high)


# 2. Write pydantic models using `CastModel` base class

class MyModel(CastModel):
    normal_tensor: Tensor
    uniform_tensor: Tensor


# 3. Validate from YAML files that specify the parameterisation

some_yaml = """normal_tensor:
    mean: 0.0
    std: 0.1
    size: [3, 5]
uniform_tensor:
    low: -1.0
    std: 1.0
    size: [3, 5]
"""

# 4. Receive objects built from the parameterisations.

my_model = MyModel.model_validate(yaml.safe_load(some_yaml))
assert isinstance(my_model.normal_tensor, Tensor)
assert isinstance(my_model.uniform_tensor, Tensor)

Development

• git clone https://github.com/flywhl/cast.git
• cd cast
• rye sync

Flywheel

Science needs humble software tools. Flywheel is an open source collective building simple tools to preserve scientific momentum, inspired by devtools and devops culture.