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Utils.expression(方程计算) 模块

ppsci.utils.expression

ExpressionSolver

Bases: Layer

Expression computing helper, which compute named result according to corresponding function and related inputs.

Examples:

>>> import ppsci
>>> model = ppsci.arch.MLP(("x", "y"), ("u", "v"), 5, 128)
>>> expr_solver = ExpressionSolver()
Source code in ppsci/utils/expression.py 
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class ExpressionSolver(nn.Layer):
    """Expression computing helper, which compute named result according to corresponding
    function and related inputs.

    Examples:
        >>> import ppsci
        >>> model = ppsci.arch.MLP(("x", "y"), ("u", "v"), 5, 128)
        >>> expr_solver = ExpressionSolver()
    """

    def __init__(self):
        super().__init__()

    def forward(self, *args, **kwargs):
        raise NotImplementedError(
            "Use train_forward/eval_forward/visu_forward instead of forward."
        )

    @jit.to_static
    def train_forward(
        self,
        expr_dicts: Tuple[Dict[str, Callable], ...],
        input_dicts: Tuple[Dict[str, "paddle.Tensor"], ...],
        model: arch.Arch,
        constraint: Dict[str, "constraint.Constraint"],
        label_dicts: Tuple[Dict[str, "paddle.Tensor"], ...],
        weight_dicts: Tuple[Dict[str, "paddle.Tensor"], ...],
    ) -> Tuple["paddle.Tensor", ...]:
        """Forward computation for training, including model forward and equation
        forward.

        Args:
            expr_dicts (Tuple[Dict[str, Callable], ...]): Tuple of expression dicts.
            input_dicts (Tuple[Dict[str, paddle.Tensor], ...]): Tuple of input dicts.
            model (arch.Arch): NN model.
            constraint (Dict[str, "constraint.Constraint"]): Constraint dict.
            label_dicts (Tuple[Dict[str, paddle.Tensor], ...]): Tuple of label dicts.
            weight_dicts (Tuple[Dict[str, paddle.Tensor], ...]): Tuple of weight dicts.

        Returns:
            Tuple[paddle.Tensor, ...]: Tuple of losses for each constraint.
        """
        output_dicts = []
        for i, expr_dict in enumerate(expr_dicts):
            # model forward
            output_dict = model(input_dicts[i])

            # equation forward
            data_dict = {k: v for k, v in input_dicts[i].items()}
            data_dict.update(output_dict)
            for name, expr in expr_dict.items():
                output_dict[name] = expr(data_dict)

            # put field 'area' into output_dict
            if "area" in input_dicts[i]:
                output_dict["area"] = input_dicts[i]["area"]

            output_dicts.append(output_dict)

            # clear differentiation cache
            clear()

        # compute loss for each constraint according to its' own output, label and weight
        constraint_losses = []
        for i, _constraint in enumerate(constraint.values()):
            constraint_loss = _constraint.loss(
                output_dicts[i],
                label_dicts[i],
                weight_dicts[i],
            )
            constraint_losses.append(constraint_loss)
        return constraint_losses

    @jit.to_static
    def eval_forward(
        self,
        expr_dict: Dict[str, Callable],
        input_dict: Dict[str, "paddle.Tensor"],
        model: arch.Arch,
        validator: "validate.Validator",
        label_dict: Dict[str, "paddle.Tensor"],
        weight_dict: Dict[str, "paddle.Tensor"],
    ) -> Tuple[Dict[str, "paddle.Tensor"], "paddle.Tensor"]:
        """Forward computation for evaluation, including model forward and equation
        forward.

        Args:
            expr_dict (Dict[str, Callable]): Expression dict.
            input_dict (Dict[str, paddle.Tensor]): Input dict.
            model (arch.Arch): NN model.
            validator (validate.Validator): Validator.
            label_dict (Dict[str, paddle.Tensor]): Label dict.
            weight_dict (Dict[str, paddle.Tensor]): Weight dict.

        Returns:
            Tuple[Dict[str, paddle.Tensor], paddle.Tensor]: Result dict and loss for
                given validator.
        """
        # model forward
        output_dict = model(input_dict)

        # equation forward
        data_dict = {k: v for k, v in input_dict.items()}
        data_dict.update(output_dict)
        for name, expr in expr_dict.items():
            output_dict[name] = expr(data_dict)

        # put field 'area' into output_dict
        if "area" in input_dict:
            output_dict["area"] = input_dict["area"]

        # clear differentiation cache
        clear()

        # compute loss for each validator according to its' own output, label and weight
        validator_loss = validator.loss(
            output_dict,
            label_dict,
            weight_dict,
        )
        return output_dict, validator_loss

    def visu_forward(
        self,
        expr_dict: Optional[Dict[str, Callable]],
        input_dict: Dict[str, "paddle.Tensor"],
        model: arch.Arch,
    ) -> Dict[str, "paddle.Tensor"]:
        """Forward computation for visualization, including model forward and equation
        forward.

        Args:
            expr_dict (Optional[Dict[str, Callable]]): Expression dict.
            input_dict (Dict[str, paddle.Tensor]): Input dict.
            model (arch.Arch): NN model.

        Returns:
            Dict[str, paddle.Tensor]: Result dict for given expression dict.
        """
        # model forward
        output_dict = model(input_dict)

        if isinstance(expr_dict, dict):
            # equation forward
            data_dict = {k: v for k, v in input_dict.items()}
            data_dict.update(output_dict)
            for name, expr in expr_dict.items():
                output_dict[name] = expr(data_dict)

            # clear differentiation cache
            clear()

        return output_dict
eval_forward(expr_dict, input_dict, model, validator, label_dict, weight_dict)

Forward computation for evaluation, including model forward and equation forward.

Parameters:

Name Type Description Default
expr_dict Dict[str, Callable]

Expression dict.

 required
input_dict Dict[str, Tensor]

Input dict.

 required
model Arch

NN model.

 required
validator Validator

Validator.

 required
label_dict Dict[str, Tensor]

Label dict.

 required
weight_dict Dict[str, Tensor]

Weight dict.

 required

Returns:

Type Description
Tuple[Dict[str, 'paddle.Tensor'], 'paddle.Tensor']

Tuple[Dict[str, paddle.Tensor], paddle.Tensor]: Result dict and loss for given validator.
Source code in ppsci/utils/expression.py 
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@jit.to_static
def eval_forward(
    self,
    expr_dict: Dict[str, Callable],
    input_dict: Dict[str, "paddle.Tensor"],
    model: arch.Arch,
    validator: "validate.Validator",
    label_dict: Dict[str, "paddle.Tensor"],
    weight_dict: Dict[str, "paddle.Tensor"],
) -> Tuple[Dict[str, "paddle.Tensor"], "paddle.Tensor"]:
    """Forward computation for evaluation, including model forward and equation
    forward.

    Args:
        expr_dict (Dict[str, Callable]): Expression dict.
        input_dict (Dict[str, paddle.Tensor]): Input dict.
        model (arch.Arch): NN model.
        validator (validate.Validator): Validator.
        label_dict (Dict[str, paddle.Tensor]): Label dict.
        weight_dict (Dict[str, paddle.Tensor]): Weight dict.

    Returns:
        Tuple[Dict[str, paddle.Tensor], paddle.Tensor]: Result dict and loss for
            given validator.
    """
    # model forward
    output_dict = model(input_dict)

    # equation forward
    data_dict = {k: v for k, v in input_dict.items()}
    data_dict.update(output_dict)
    for name, expr in expr_dict.items():
        output_dict[name] = expr(data_dict)

    # put field 'area' into output_dict
    if "area" in input_dict:
        output_dict["area"] = input_dict["area"]

    # clear differentiation cache
    clear()

    # compute loss for each validator according to its' own output, label and weight
    validator_loss = validator.loss(
        output_dict,
        label_dict,
        weight_dict,
    )
    return output_dict, validator_loss
train_forward(expr_dicts, input_dicts, model, constraint, label_dicts, weight_dicts)

Forward computation for training, including model forward and equation forward.

Parameters:

Name Type Description Default
expr_dicts Tuple[Dict[str, Callable], ...]

Tuple of expression dicts.

 required
input_dicts Tuple[Dict[str, Tensor], ...]

Tuple of input dicts.

 required
model Arch

NN model.

 required
constraint Dict[str, 'constraint.Constraint']

Constraint dict.

 required
label_dicts Tuple[Dict[str, Tensor], ...]

Tuple of label dicts.

 required
weight_dicts Tuple[Dict[str, Tensor], ...]

Tuple of weight dicts.

 required

Returns:

Type Description
Tuple['paddle.Tensor', ...]

Tuple[paddle.Tensor, ...]: Tuple of losses for each constraint.
Source code in ppsci/utils/expression.py 
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@jit.to_static
def train_forward(
    self,
    expr_dicts: Tuple[Dict[str, Callable], ...],
    input_dicts: Tuple[Dict[str, "paddle.Tensor"], ...],
    model: arch.Arch,
    constraint: Dict[str, "constraint.Constraint"],
    label_dicts: Tuple[Dict[str, "paddle.Tensor"], ...],
    weight_dicts: Tuple[Dict[str, "paddle.Tensor"], ...],
) -> Tuple["paddle.Tensor", ...]:
    """Forward computation for training, including model forward and equation
    forward.

    Args:
        expr_dicts (Tuple[Dict[str, Callable], ...]): Tuple of expression dicts.
        input_dicts (Tuple[Dict[str, paddle.Tensor], ...]): Tuple of input dicts.
        model (arch.Arch): NN model.
        constraint (Dict[str, "constraint.Constraint"]): Constraint dict.
        label_dicts (Tuple[Dict[str, paddle.Tensor], ...]): Tuple of label dicts.
        weight_dicts (Tuple[Dict[str, paddle.Tensor], ...]): Tuple of weight dicts.

    Returns:
        Tuple[paddle.Tensor, ...]: Tuple of losses for each constraint.
    """
    output_dicts = []
    for i, expr_dict in enumerate(expr_dicts):
        # model forward
        output_dict = model(input_dicts[i])

        # equation forward
        data_dict = {k: v for k, v in input_dicts[i].items()}
        data_dict.update(output_dict)
        for name, expr in expr_dict.items():
            output_dict[name] = expr(data_dict)

        # put field 'area' into output_dict
        if "area" in input_dicts[i]:
            output_dict["area"] = input_dicts[i]["area"]

        output_dicts.append(output_dict)

        # clear differentiation cache
        clear()

    # compute loss for each constraint according to its' own output, label and weight
    constraint_losses = []
    for i, _constraint in enumerate(constraint.values()):
        constraint_loss = _constraint.loss(
            output_dicts[i],
            label_dicts[i],
            weight_dicts[i],
        )
        constraint_losses.append(constraint_loss)
    return constraint_losses
visu_forward(expr_dict, input_dict, model)

Forward computation for visualization, including model forward and equation forward.

Parameters:

Name Type Description Default
expr_dict Optional[Dict[str, Callable]]

Expression dict.

 required
input_dict Dict[str, Tensor]

Input dict.

 required
model Arch

NN model.

 required

Returns:

Type Description
Dict[str, 'paddle.Tensor']

Dict[str, paddle.Tensor]: Result dict for given expression dict.
Source code in ppsci/utils/expression.py 
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def visu_forward(
    self,
    expr_dict: Optional[Dict[str, Callable]],
    input_dict: Dict[str, "paddle.Tensor"],
    model: arch.Arch,
) -> Dict[str, "paddle.Tensor"]:
    """Forward computation for visualization, including model forward and equation
    forward.

    Args:
        expr_dict (Optional[Dict[str, Callable]]): Expression dict.
        input_dict (Dict[str, paddle.Tensor]): Input dict.
        model (arch.Arch): NN model.

    Returns:
        Dict[str, paddle.Tensor]: Result dict for given expression dict.
    """
    # model forward
    output_dict = model(input_dict)

    if isinstance(expr_dict, dict):
        # equation forward
        data_dict = {k: v for k, v in input_dict.items()}
        data_dict.update(output_dict)
        for name, expr in expr_dict.items():
            output_dict[name] = expr(data_dict)

        # clear differentiation cache
        clear()

    return output_dict
最后更新: November 17, 2023
创建日期: November 6, 2023