Python jit.ScriptModule方法代碼示例

# 需要導入模塊: from torch import jit [as 別名]
# 或者: from torch.jit import ScriptModule [as 別名]
def script_lstm(input_size, hidden_size, num_layers, bias=True,
                batch_first=False, dropout=False, bidirectional=False):
    '''Returns a ScriptModule that mimics a PyTorch native LSTM.'''

    # The following are not implemented.
    assert bias
    assert not batch_first

    if bidirectional:
        stack_type = StackedLSTM2
        layer_type = BidirLSTMLayer
        dirs = 2
    elif dropout:
        stack_type = StackedLSTMWithDropout
        layer_type = LSTMLayer
        dirs = 1
    else:
        stack_type = StackedLSTM
        layer_type = LSTMLayer
        dirs = 1

    return stack_type(num_layers, layer_type,
                      first_layer_args=[LSTMCell, input_size, hidden_size],
                      other_layer_args=[LSTMCell, hidden_size * dirs,
                                        hidden_size]) 

# 需要導入模塊: from torch import jit [as 別名]
# 或者: from torch.jit import ScriptModule [as 別名]
def script_lnlstm(input_size, hidden_size, num_layers, bias=True,
                  batch_first=False, dropout=False, bidirectional=False,
                  decompose_layernorm=False):
    '''Returns a ScriptModule that mimics a PyTorch native LSTM.'''

    # The following are not implemented.
    assert bias
    assert not batch_first
    assert not dropout

    if bidirectional:
        stack_type = StackedLSTM2
        layer_type = BidirLSTMLayer
        dirs = 2
    else:
        stack_type = StackedLSTM
        layer_type = LSTMLayer
        dirs = 1

    return stack_type(num_layers, layer_type,
                      first_layer_args=[LayerNormLSTMCell, input_size, hidden_size,
                                        decompose_layernorm],
                      other_layer_args=[LayerNormLSTMCell, hidden_size * dirs,
                                        hidden_size, decompose_layernorm]) 

# 需要導入模塊: from torch import jit [as 別名]
# 或者: from torch.jit import ScriptModule [as 別名]
def load_traced_model(
    model_path: Union[str, Path],
    device: Device = "cpu",
    opt_level: str = None,
) -> jit.ScriptModule:
    """Loads a traced model.

    Args:
        model_path: Path to traced model
        device (str): Torch device
        opt_level (str): Apex FP16 init level, optional

    Returns:
        ScriptModule: Traced model
    """
    # jit.load dont work with pathlib.Path
    model_path = str(model_path)

    if opt_level is not None:
        device = "cuda"

    model = jit.load(model_path, map_location=device)

    if opt_level is not None:
        assert_fp16_available()
        from apex import amp

        model = amp.initialize(model, optimizers=None, opt_level=opt_level)

    return model 

# 需要導入模塊: from torch import jit [as 別名]
# 或者: from torch.jit import ScriptModule [as 別名]
def trace_model(
    model: Model,
    predict_fn: Callable,
    batch=None,
    method_name: str = "forward",
    mode: str = "eval",
    requires_grad: bool = False,
    opt_level: str = None,
    device: Device = "cpu",
    predict_params: dict = None,
) -> jit.ScriptModule:
    """Traces model using runner and batch.

    Args:
        model: Model to trace
        predict_fn: Function to run prediction with the model provided,
            takes model, inputs parameters
        batch: Batch to trace the model
        method_name (str): Model's method name that will be
            used as entrypoint during tracing
        mode (str): Mode for model to trace (``train`` or ``eval``)
        requires_grad (bool): Flag to use grads
        opt_level (str): Apex FP16 init level, optional
        device (str): Torch device
        predict_params (dict): additional parameters for model forward

    Returns:
        jit.ScriptModule: Traced model

    Raises:
        ValueError: if both batch and predict_fn must be specified or
          mode is not in 'eval' or 'train'.
    """
    if batch is None or predict_fn is None:
        raise ValueError("Both batch and predict_fn must be specified.")

    if mode not in ["train", "eval"]:
        raise ValueError(f"Unknown mode '{mode}'. Must be 'eval' or 'train'")

    predict_params = predict_params or {}

    tracer = _TracingModelWrapper(model, method_name)
    if opt_level is not None:
        assert_fp16_available()
        # If traced in AMP we need to initialize the model before calling
        # the jit
        # https://github.com/NVIDIA/apex/issues/303#issuecomment-493142950
        from apex import amp

        model = model.to(device)
        model = amp.initialize(model, optimizers=None, opt_level=opt_level)

    getattr(model, mode)()
    set_requires_grad(model, requires_grad=requires_grad)

    predict_fn(tracer, batch, **predict_params)

    return tracer.tracing_result 

# 需要導入模塊: from torch import jit [as 別名]
# 或者: from torch.jit import ScriptModule [as 別名]
def save_traced_model(
    model: jit.ScriptModule,
    logdir: Union[str, Path] = None,
    method_name: str = "forward",
    mode: str = "eval",
    requires_grad: bool = False,
    opt_level: str = None,
    out_dir: Union[str, Path] = None,
    out_model: Union[str, Path] = None,
    checkpoint_name: str = None,
) -> None:
    """Saves traced model.

    Args:
        model (ScriptModule): Traced model
        logdir (Union[str, Path]): Path to experiment
        method_name (str): Name of the method was traced
        mode (str): Model's mode - `train` or `eval`
        requires_grad (bool): Whether model was traced with require_grad or not
        opt_level (str): Apex FP16 init level used during tracing
        out_dir (Union[str, Path]): Directory to save model to
            (overrides logdir)
        out_model (Union[str, Path]): Path to save model to
            (overrides logdir & out_dir)
        checkpoint_name (str): Checkpoint name used to restore the model

    Raises:
        ValueError: if nothing out of `logdir`, `out_dir` or `out_model`
          is specified.
    """
    if out_model is None:
        file_name = get_trace_name(
            method_name=method_name,
            mode=mode,
            requires_grad=requires_grad,
            opt_level=opt_level,
            additional_string=checkpoint_name,
        )

        output: Path = out_dir
        if output is None:
            if logdir is None:
                raise ValueError(
                    "One of `logdir`, `out_dir` or `out_model` "
                    "should be specified"
                )
            output: Path = Path(logdir) / "trace"

        output.mkdir(exist_ok=True, parents=True)

        out_model = str(output / file_name)
    else:
        out_model = str(out_model)

    jit.save(model, out_model)