Models

Create Model

mindcv.models.model_factory.create_model(model_name, num_classes=1000, pretrained=False, in_channels=3, checkpoint_path='', ema=False, auto_mapping=False, **kwargs)

Creates model by name.

PARAMETER	DESCRIPTION
model_name	The name of model. TYPE: str
num_classes	The number of classes. Default: 1000. TYPE: int DEFAULT: 1000
pretrained	Whether to load the pretrained model. Default: False. TYPE: bool DEFAULT: False
in_channels	The input channels. Default: 3. TYPE: int DEFAULT: 3
checkpoint_path	The path of checkpoint files. Default: "". TYPE: str DEFAULT: ''
ema	Whether use ema method. Default: False. TYPE: bool DEFAULT: False
auto_mapping	Whether to automatically map the names of checkpoint weights to the names of model weights when there are differences in names. Default: False. TYPE: bool DEFAULT: False
**kwargs	additional args, e.g., "features_only", "out_indices". DEFAULT: {}

Source code in mindcv/models/model_factory.py

def create_model(
    model_name: str,
    num_classes: int = 1000,
    pretrained: bool = False,
    in_channels: int = 3,
    checkpoint_path: str = "",
    ema: bool = False,
    auto_mapping: bool = False,
    **kwargs,
):
    r"""Creates model by name.

    Args:
        model_name (str):  The name of model.
        num_classes (int): The number of classes. Default: 1000.
        pretrained (bool): Whether to load the pretrained model. Default: False.
        in_channels (int): The input channels. Default: 3.
        checkpoint_path (str): The path of checkpoint files. Default: "".
        ema (bool): Whether use ema method. Default: False.
        auto_mapping (bool): Whether to automatically map the names of checkpoint weights
            to the names of model weights when there are differences in names. Default: False.
        **kwargs: additional args, e.g., "features_only", "out_indices".
    """

    if checkpoint_path != "" and pretrained:
        raise ValueError("checkpoint_path is mutually exclusive with pretrained")

    model_args = dict(num_classes=num_classes, pretrained=pretrained, in_channels=in_channels)
    kwargs = {k: v for k, v in kwargs.items() if v is not None}

    if not is_model(model_name):
        raise RuntimeError(f"Unknown model {model_name}")

    create_fn = model_entrypoint(model_name)
    model = create_fn(**model_args, **kwargs)

    if checkpoint_path:
        load_model_checkpoint(model, checkpoint_path, ema, auto_mapping)

    return model

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