Python numpy.dtype方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def __init__(self, resolution=1024, num_channels=3, dtype='uint8', dynamic_range=[0,255], label_size=0, label_dtype='float32'):
        self.resolution         = resolution
        self.resolution_log2    = int(np.log2(resolution))
        self.shape              = [num_channels, resolution, resolution]
        self.dtype              = dtype
        self.dynamic_range      = dynamic_range
        self.label_size         = label_size
        self.label_dtype        = label_dtype
        self._tf_minibatch_var  = None
        self._tf_lod_var        = None
        self._tf_minibatch_np   = None
        self._tf_labels_np      = None

        assert self.resolution == 2 ** self.resolution_log2
        with tf.name_scope('Dataset'):
            self._tf_minibatch_var = tf.Variable(np.int32(0), name='minibatch_var')
            self._tf_lod_var = tf.Variable(np.int32(0), name='lod_var') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def serialize_dtype(o):
    """
    Serializes a :obj:`numpy.dtype`.

    Args:
        o (:obj:`numpy.dtype`): :obj:`dtype` to be serialized.

    Returns:
        A dictionary that can be passed to :obj:`json.dumps`.
    """
    if len(o) == 0:
        return dict(
            _type='np.dtype',
            descr=str(o))
    return dict(
        _type='np.dtype',
        descr=o.descr)
    # res = []
    # for k in range(len(o)):
    #     res.append((o.names[k], str(o[k])))
    # return dict(
    #     _type='np.dtype',
    #     desc=res) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def deserialize_dtype(d):
    """
    Deserializes a JSONified :obj:`numpy.dtype`.

    Args:
        d (:obj:`dict`): A dictionary representation of a :obj:`dtype` object.

    Returns:
        A :obj:`dtype` object.
    """
    if isinstance(d['descr'], six.string_types):
        return np.dtype(d['descr'])
    descr = []
    for col in d['descr']:
        col_descr = []
        for c in col:
            if isinstance(c, six.string_types):
                col_descr.append(str(c))
            elif type(c) is list:
                col_descr.append(tuple(c))
            else:
                col_descr.append(c)
        descr.append(tuple(col_descr))
    return np.dtype(descr) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def serialize_ndarray_b64(o):
    """
    Serializes a :obj:`numpy.ndarray` in a format where the datatype and shape are
    human-readable, but the array data itself is binary64 encoded.

    Args:
        o (:obj:`numpy.ndarray`): :obj:`ndarray` to be serialized.

    Returns:
        A dictionary that can be passed to :obj:`json.dumps`.
    """
    if o.flags['C_CONTIGUOUS']:
        o_data = o.data
    else:
        o_data = np.ascontiguousarray(o).data
    data_b64 = base64.b64encode(o_data)
    return dict(
        _type='np.ndarray',
        data=data_b64.decode('utf-8'),
        dtype=o.dtype,
        shape=o.shape) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def deserialize_ndarray(d):
    """
    Deserializes a JSONified :obj:`numpy.ndarray`. Can handle arrays serialized
    using any of the methods in this module: :obj:`"npy"`, :obj:`"b64"`,
    :obj:`"readable"`.

    Args:
        d (`dict`): A dictionary representation of an :obj:`ndarray` object.

    Returns:
        An :obj:`ndarray` object.
    """
    if 'data' in d:
        x = np.fromstring(
            base64.b64decode(d['data']),
            dtype=d['dtype'])
        x.shape = d['shape']
        return x
    elif 'value' in d:
        return np.array(d['value'], dtype=d['dtype'])
    elif 'npy' in d:
        return deserialize_ndarray_npy(d)
    else:
        raise ValueError('Malformed np.ndarray encoding.') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def __init__(self, model, dtypestr='float32'):
        """
        :param model: An instance of the cleverhans.model.Model class.
        :param back: The backend to use. Inherited from AttackBase class.
        :param dtypestr: datatype of the input data samples and crafted
                        adversarial attacks.
        """
        # Validate the input arguments.
        if dtypestr != 'float32' and dtypestr != 'float64':
            raise ValueError("Unexpected input for argument dtypestr.")
        import tensorflow as tf
        tfe = tf.contrib.eager
        self.tf_dtype = tf.as_dtype(dtypestr)
        self.np_dtype = np.dtype(dtypestr)

        if not isinstance(model, Model):
            raise ValueError("The model argument should be an instance of"
                             " the cleverhans.model.Model class.")
        # Prepare attributes
        self.model = model
        self.dtypestr = dtypestr 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def image_reslice(image, spec, method=None, fill=0, dtype=None, weights=None, image_type=None):
    '''
    image_reslice(image, spec) yields a duplicate of the given image resliced to have the voxels
      indicated by the given image spec. Note that spec may be an image itself.

    Optional arguments that can be passed to image_interpolate() (asside from affine) are allowed
    here and are passed through.
    '''
    if image_type is None and is_image(image): image_type = to_image_type(image)
    spec = to_image_spec(spec)
    image = to_image(image)
    # we make a big mesh and interpolate at these points...
    imsh = spec['image_shape']
    (args, kw) = ([np.arange(n) for n in imsh[:3]], {'indexing': 'ij'})
    ijk = np.asarray([u.flatten() for u in np.meshgrid(*args, **kw)])
    ijk = np.dot(spec['affine'], np.vstack([ijk, np.ones([1,ijk.shape[1]])]))[:3]
    # interpolate here...
    u = image_interpolate(image, ijk, method=method, fill=fill, dtype=dtype, weights=weights)
    return to_image((np.reshape(u, imsh), spec), image_type=image_type) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def astype(self, dtype, copy=True):
        """Returns a copy of the array after casting to a specified type.
        Parameters
        ----------
        dtype : numpy.dtype or str
            The type of the returned array.
        copy : bool
            Default `True`. By default, astype always returns a newly
            allocated ndarray on the same context. If this is set to
            `False`, and the dtype requested is the same as the ndarray's
            dtype, the ndarray is returned instead of a copy.
        Examples
        --------
        >>> x = mx.nd.sparse.zeros('row_sparse', (2,3), dtype='float32')
        >>> y = x.astype('int32')
        >>> y.dtype
        <type 'numpy.int32'>
        """
        if not copy and np.dtype(dtype) == self.dtype:
            return self

        res = zeros(shape=self.shape, ctx=self.context,
                    dtype=dtype, stype=self.stype)
        self.copyto(res)
        return res 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def asscipy(self):
        """Returns a ``scipy.sparse.csr.csr_matrix`` object with value copied from this array

        Examples
        --------
        >>> x = mx.nd.sparse.zeros('csr', (2,3))
        >>> y = x.asscipy()
        >>> type(y)
        <type 'scipy.sparse.csr.csr_matrix'>
        >>> y
        <2x3 sparse matrix of type '<type 'numpy.float32'>'
        with 0 stored elements in Compressed Sparse Row format>
        """
        data = self.data.asnumpy()
        indices = self.indices.asnumpy()
        indptr = self.indptr.asnumpy()
        if not spsp:
            raise ImportError("scipy is not available. \
                               Please check if the scipy python bindings are installed.")
        return spsp.csr_matrix((data, indices, indptr), shape=self.shape, dtype=self.dtype)

# pylint: disable=abstract-method 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
    """Return a new handle with specified shape and context.

    Empty handle is only used to hold results.

    Returns
    -------
    handle
        A new empty `NDArray` handle.
    """
    hdl = NDArrayHandle()
    check_call(_LIB.MXNDArrayCreateEx(
        c_array_buf(mx_uint, native_array('I', shape)),
        mx_uint(len(shape)),
        ctypes.c_int(ctx.device_typeid),
        ctypes.c_int(ctx.device_id),
        ctypes.c_int(int(delay_alloc)),
        ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
        ctypes.byref(hdl)))
    return hdl 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def _prepare_value_nd(self, value, vshape):
        """Given value and vshape, create an `NDArray` from value with the same
        context and dtype as the current one and broadcast it to vshape."""
        if isinstance(value, numeric_types):
            value_nd = full(shape=vshape, val=value, ctx=self.context, dtype=self.dtype)
        elif isinstance(value, NDArray):
            value_nd = value.as_in_context(self.context)
            if value_nd.dtype != self.dtype:
                value_nd = value_nd.astype(self.dtype)
        else:
            try:
                value_nd = array(value, ctx=self.context, dtype=self.dtype)
            except:
                raise TypeError('NDArray does not support assignment with non-array-like'
                                ' object %s of type %s' % (str(value), str(type(value))))
        if value_nd.shape != vshape:
            value_nd = value_nd.broadcast_to(vshape)
        return value_nd 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def dtype(self):
        """Data-type of the array's elements.

        Returns
        -------
        numpy.dtype
            This NDArray's data type.

        Examples
        --------
        >>> x = mx.nd.zeros((2,3))
        >>> x.dtype
        <type 'numpy.float32'>
        >>> y = mx.nd.zeros((2,3), dtype='int32')
        >>> y.dtype
        <type 'numpy.int32'>
        """
        mx_dtype = ctypes.c_int()
        check_call(_LIB.MXNDArrayGetDType(
            self.handle, ctypes.byref(mx_dtype)))
        return _DTYPE_MX_TO_NP[mx_dtype.value] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def T(self):
        """Returns a copy of the array with axes transposed.

        Equivalent to ``mx.nd.transpose(self)`` except that
        self is returned if ``self.ndim < 2``.

        Unlike ``numpy.ndarray.T``, this function returns a copy
        rather than a view of the array unless ``self.ndim < 2``.

        Examples
        --------
        >>> x = mx.nd.arange(0,6).reshape((2,3))
        >>> x.asnumpy()
        array([[ 0.,  1.,  2.],
               [ 3.,  4.,  5.]], dtype=float32)
        >>> x.T.asnumpy()
        array([[ 0.,  3.],
               [ 1.,  4.],
               [ 2.,  5.]], dtype=float32)

        """
        if len(self.shape) < 2:
            return self
        return op.transpose(self)
    # pylint: enable= invalid-name, undefined-variable 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def asnumpy(self):
        """Returns a ``numpy.ndarray`` object with value copied from this array.

        Examples
        --------
        >>> x = mx.nd.ones((2,3))
        >>> y = x.asnumpy()
        >>> type(y)
        <type 'numpy.ndarray'>
        >>> y
        array([[ 1.,  1.,  1.],
               [ 1.,  1.,  1.]], dtype=float32)
        >>> z = mx.nd.ones((2,3), dtype='int32')
        >>> z.asnumpy()
        array([[1, 1, 1],
               [1, 1, 1]], dtype=int32)
        """
        data = np.empty(self.shape, dtype=self.dtype)
        check_call(_LIB.MXNDArraySyncCopyToCPU(
            self.handle,
            data.ctypes.data_as(ctypes.c_void_p),
            ctypes.c_size_t(data.size)))
        return data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import dtype [as 別名]
def asscalar(self):
        """Returns a scalar whose value is copied from this array.

        This function is equivalent to ``self.asnumpy()[0]``. This NDArray must have shape (1,).

        Examples
        --------
        >>> x = mx.nd.ones((1,), dtype='int32')
        >>> x.asscalar()
        1
        >>> type(x.asscalar())
        <type 'numpy.int32'>
        """
        if self.shape != (1,):
            raise ValueError("The current array is not a scalar")
        return self.asnumpy()[0]