Python base.string_types方法代码示例

# 需要导入模块: from mxnet import base [as 别名]
# 或者: from mxnet.base import string_types [as 别名]
def _parse_default(self, target):
        """Helper function to parse default values."""
        if not isinstance(target, (list, tuple)):
            k, v, t = target, None, lambda x: x
        elif len(target) == 1:
            k, v, t = target[0], None, lambda x: x
        elif len(target) == 2:
            k, v, t = target[0], target[1], lambda x: x
        elif len(target) > 2:
            k, v, t = target[0], target[1], target[2]
        else:
            k = None  # should raise
        if not isinstance(k, string_types):
            msg = "{} is not a valid target, (name, default) expected.".format(target)
            raise ValueError(msg)
        return k, v, t 

# 需要导入模块: from mxnet import base [as 别名]
# 或者: from mxnet.base import string_types [as 别名]
def __call__(self, attrs):
        # apply custom check
        if self._custom_check:
            func, msg = self._custom_check
            if not func(attrs):
                raise RuntimeError("Check failed: {}".format(msg))
        # get new op_name
        if isinstance(self._op_name, string_types):
            op_name = self._op_name
        else:
            assert callable(self._op_name), "op_name can either be string or callable"
            op_name = self._op_name(attrs)
        # convert attributes
        new_attrs = {}
        for k in attrs.keys():
            if k in self._excludes:
                raise NotImplementedError("Attribute {} not supported yet.".format(k))
            elif k in self._ignores:
                pass
            elif k in self._transforms:
                new_name, defaults, transform = self._parse_default(self._transforms[k])
                if defaults is None:
                    new_attr = self._required_attr(attrs, k)
                else:
                    new_attr = attrs.get(k, None)
                if new_attr is None:
                    new_attrs[new_name] = defaults
                else:
                    new_attrs[new_name] = transform(new_attr)
            else:
                # copy
                new_attrs[k] = attrs[k]
        # add extras
        new_attrs.update(self._extras)
        return op_name, new_attrs 

# 需要导入模块: from mxnet import base [as 别名]
# 或者: from mxnet.base import string_types [as 别名]
def _parse_bool(self, value):
        """Helper function to parse default boolean values."""
        if isinstance(value, string_types):
            return value.strip().lower() in ['true', '1', 't', 'y', 'yes']
        return bool(value) 

# 需要导入模块: from mxnet import base [as 别名]
# 或者: from mxnet.base import string_types [as 别名]
def _parse_network(network, outputs, inputs, pretrained, ctx, **kwargs):
    """Parse network with specified outputs and other arguments.

    Parameters
    ----------
    network : str or HybridBlock or Symbol
        Logic chain: load from gluoncv.model_zoo if network is string.
        Convert to Symbol if network is HybridBlock
    outputs : str or iterable of str
        The name of layers to be extracted as features.
    inputs : iterable of str
        The name of input datas.
    pretrained : bool
        Use pretrained parameters as in gluon.model_zoo
    ctx : Context
        The context, e.g. mxnet.cpu(), mxnet.gpu(0).

    Returns
    -------
    inputs : list of Symbol
        Network input Symbols, usually ['data']
    outputs : list of Symbol
        Network output Symbols, usually as features
    params : ParameterDict
        Network parameters.
    """
    inputs = list(inputs) if isinstance(inputs, tuple) else inputs
    for i, inp in enumerate(inputs):
        if isinstance(inp, string_types):
            inputs[i] = mx.sym.var(inp)
        assert isinstance(inputs[i], Symbol), "Network expects inputs are Symbols."
    if len(inputs) == 1:
        inputs = inputs[0]
    else:
        inputs = mx.sym.Group(inputs)
    params = None
    prefix = ''
    if isinstance(network, string_types):
        from ..model_zoo import get_model
        network = get_model(network, pretrained=pretrained, ctx=ctx, **kwargs)
    if isinstance(network, HybridBlock):
        params = network.collect_params()
        prefix = network._prefix
        network = network(inputs)
    assert isinstance(network, Symbol), \
        "FeatureExtractor requires the network argument to be either " \
        "str, HybridBlock or Symbol, but got %s" % type(network)

    if isinstance(outputs, string_types):
        outputs = [outputs]
    assert len(outputs) > 0, "At least one outputs must be specified."
    outputs = [out if out.endswith('_output') else out + '_output' for out in outputs]
    outputs = [network.get_internals()[prefix + out] for out in outputs]
    return inputs, outputs, params 

# 需要导入模块: from mxnet import base [as 别名]
# 或者: from mxnet.base import string_types [as 别名]
def _parse_network(network, outputs, inputs, pretrained, ctx):
    """Parse network with specified outputs and other arguments.

    Parameters
    ----------
    network : str or HybridBlock or Symbol
        Logic chain: load from gluon.model_zoo.vision if network is string.
        Convert to Symbol if network is HybridBlock
    outputs : str or iterable of str
        The name of layers to be extracted as features.
    inputs : iterable of str
        The name of input datas.
    pretrained : bool
        Use pretrained parameters as in gluon.model_zoo
    ctx : Context
        The context, e.g. mxnet.cpu(), mxnet.gpu(0).

    Returns
    -------
    inputs : list of Symbol
        Network input Symbols, usually ['data']
    outputs : list of Symbol
        Network output Symbols, usually as features
    params : ParameterDict
        Network parameters.
    """
    inputs = list(inputs) if isinstance(inputs, tuple) else inputs
    for i, inp in enumerate(inputs):
        if isinstance(inp, string_types):
            inputs[i] = mx.sym.var(inp)
        assert isinstance(inputs[i], Symbol), "Network expects inputs are Symbols."
    if len(inputs) == 1:
        inputs = inputs[0]
    else:
        inputs = mx.sym.Group(inputs)
    params = None
    prefix = ''
    if isinstance(network, string_types):
        network = vision.get_model(network, pretrained=pretrained, ctx=ctx)
    if isinstance(network, HybridBlock):
        params = network.collect_params()
        prefix = network._prefix
        network = network(inputs)
    assert isinstance(network, Symbol), \
        "FeatureExtractor requires the network argument to be either " \
        "str, HybridBlock or Symbol, but got %s"%type(network)

    if isinstance(outputs, string_types):
        outputs = [outputs]
    assert len(outputs) > 0, "At least one outputs must be specified."
    outputs = [out if out.endswith('_output') else out + '_output' for out in outputs]
    outputs = [network.get_internals()[prefix + out] for out in outputs]
    return inputs, outputs, params