Python mxnet.nd.full() Examples

def extract_pairwise_multi_position_embedding_nd(position_mat, feat_dim, wave_length=1000):
    """ Extract multi-class position embedding

    Args:
        position_mat: [num_fg_classes, num_rois, num_rois, 4]
        feat_dim: dimension of embedding feature
        wave_length:

    Returns:
        embedding: [num_fg_classes, num_rois, num_rois, feat_dim]
    """
    feat_range = nd.arange(0, feat_dim / 8)
    dim_mat = nd.broadcast_power(lhs=nd.full((1,), wave_length),
                                     rhs=(8. / feat_dim) * feat_range)
    dim_mat = nd.Reshape(dim_mat, shape=(1, 1, 1, 1, -1))
    position_mat = nd.expand_dims(100.0 * position_mat, axis=4)
    div_mat = nd.broadcast_div(lhs=position_mat, rhs=dim_mat)
    sin_mat = nd.sin(data=div_mat)
    cos_mat = nd.cos(data=div_mat)
    # embedding, [num_fg_classes, num_rois, num_rois, 4, feat_dim/4]
    embedding = nd.concat(sin_mat, cos_mat, dim=4)
    embedding = nd.Reshape(embedding, shape=(0, 0, 0, feat_dim))
    return embedding 

def extract_pairwise_multi_position_embedding_nd(position_mat, feat_dim, wave_length=1000):
    """ Extract multi-class position embedding

    Args:
        position_mat: [num_fg_classes, num_rois, num_rois, 4]
        feat_dim: dimension of embedding feature
        wave_length:

    Returns:
        embedding: [num_fg_classes, num_rois, num_rois, feat_dim]
    """
    feat_range = nd.arange(0, feat_dim / 8)
    dim_mat = nd.broadcast_power(lhs=nd.full((1,), wave_length),
                                     rhs=(8. / feat_dim) * feat_range)
    dim_mat = nd.Reshape(dim_mat, shape=(1, 1, 1, 1, -1))
    position_mat = nd.expand_dims(100.0 * position_mat, axis=4)
    div_mat = nd.broadcast_div(lhs=position_mat, rhs=dim_mat)
    sin_mat = nd.sin(data=div_mat)
    cos_mat = nd.cos(data=div_mat)
    # embedding, [num_fg_classes, num_rois, num_rois, 4, feat_dim/4]
    embedding = nd.concat(sin_mat, cos_mat, dim=4)
    embedding = nd.Reshape(embedding, shape=(0, 0, 0, feat_dim))
    return embedding 

def extract_rank_embedding_nd(rank_dim, feat_dim, wave_length=1000):
    rank_range = nd.arange(0, rank_dim)
    feat_range = nd.arange(0, feat_dim / 2)
    dim_mat = nd.broadcast_power(lhs=nd.full((1,), wave_length),
                                     rhs=(2. / feat_dim) * feat_range)
    dim_mat = nd.Reshape(dim_mat, shape=(1, -1))
    rank_mat = nd.expand_dims(rank_range, axis=1)
    div_mat = nd.broadcast_div(lhs=rank_mat, rhs=dim_mat)
    sin_mat = nd.sin(data=div_mat)
    cos_mat = nd.cos(data=div_mat)
    embedding = nd.concat(sin_mat, cos_mat, dim=1)
    return embedding 

def extract_rank_embedding_nd(rank_dim, feat_dim, wave_length=1000):
    rank_range = nd.arange(0, rank_dim)
    feat_range = nd.arange(0, feat_dim / 2)
    dim_mat = nd.broadcast_power(lhs=nd.full((1,), wave_length),
                                     rhs=(2. / feat_dim) * feat_range)
    dim_mat = nd.Reshape(dim_mat, shape=(1, -1))
    rank_mat = nd.expand_dims(rank_range, axis=1)
    div_mat = nd.broadcast_div(lhs=rank_mat, rhs=dim_mat)
    sin_mat = nd.sin(data=div_mat)
    cos_mat = nd.cos(data=div_mat)
    embedding = nd.concat(sin_mat, cos_mat, dim=1)
    return embedding 

def random_expand(src, max_ratio=4, fill=0, keep_ratio=True):
    """Random expand original image with borders, this is identical to placing
    the original image on a larger canvas.

    Parameters
    ----------
    src : mxnet.nd.NDArray
        The original image with HWC format.
    max_ratio : int or float
        Maximum ratio of the output image on both direction(vertical and horizontal)
    fill : int or float or array-like
        The value(s) for padded borders. If `fill` is numerical type, RGB channels
        will be padded with single value. Otherwise `fill` must have same length
        as image channels, which resulted in padding with per-channel values.
    keep_ratio : bool
        If `True`, will keep output image the same aspect ratio as input.

    Returns
    -------
    mxnet.nd.NDArray
        Augmented image.
    tuple
        Tuple of (offset_x, offset_y, new_width, new_height)

    """
    if max_ratio <= 1:
        return src, (0, 0, src.shape[1], src.shape[0])

    h, w, c = src.shape
    ratio_x = random.uniform(1, max_ratio)
    if keep_ratio:
        ratio_y = ratio_x
    else:
        ratio_y = random.uniform(1, max_ratio)

    oh, ow = int(h * ratio_y), int(w * ratio_x)
    off_y = random.randint(0, oh - h)
    off_x = random.randint(0, ow - w)

    # make canvas
    if isinstance(fill, numeric_types):
        dst = nd.full(shape=(oh, ow, c), val=fill, dtype=src.dtype)
    else:
        fill = nd.array(fill, dtype=src.dtype, ctx=src.context)
        if not c == fill.size:
            raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
        dst = nd.tile(fill.reshape((1, c)), reps=(oh * ow, 1)).reshape((oh, ow, c))

    dst[off_y:off_y+h, off_x:off_x+w, :] = src
    return dst, (off_x, off_y, ow, oh) 

def resize_contain(src, size, fill=0):
    """Resize the image to fit in the given area while keeping aspect ratio.

    If both the height and the width in `size` are larger than
    the height and the width of input image, the image is placed on
    the center with an appropriate padding to match `size`.
    Otherwise, the input image is scaled to fit in a canvas whose size
    is `size` while preserving aspect ratio.

    Parameters
    ----------
    src : mxnet.nd.NDArray
        The original image with HWC format.
    size : tuple
        Tuple of length 2 as (width, height).
    fill : int or float or array-like
        The value(s) for padded borders. If `fill` is numerical type, RGB channels
        will be padded with single value. Otherwise `fill` must have same length
        as image channels, which resulted in padding with per-channel values.

    Returns
    -------
    mxnet.nd.NDArray
        Augmented image.
    tuple
        Tuple of (offset_x, offset_y, scaled_x, scaled_y)

    """
    h, w, c = src.shape
    ow, oh = size
    scale_h = oh / h
    scale_w = ow / w
    scale = min(min(scale_h, scale_w), 1)
    scaled_x = int(w * scale)
    scaled_y = int(h * scale)
    if scale < 1:
        src = mx.image.imresize(src, scaled_x, scaled_y)

    off_y = (oh - scaled_y) // 2 if scaled_y < oh else 0
    off_x = (ow - scaled_x) // 2 if scaled_x < ow else 0

    # make canvas
    if isinstance(fill, numeric_types):
        dst = nd.full(shape=(oh, ow, c), val=fill, dtype=src.dtype)
    else:
        fill = nd.array(fill, ctx=src.context)
        if not c == fill.size:
            raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
        dst = nd.repeat(fill, repeats=oh * ow).reshape((oh, ow, c))

    dst[off_y:off_y+scaled_y, off_x:off_x+scaled_x, :] = src
    return dst, (off_x, off_y, scaled_x, scaled_y) 

def random_expand(src, max_ratio=4, fill=0, keep_ratio=True):
    """Random expand original image with borders, this is identical to placing
    the original image on a larger canvas.

    Parameters
    ----------
    src : mxnet.nd.NDArray
        The original image with HWC format.
    max_ratio : int or float
        Maximum ratio of the output image on both direction(vertical and horizontal)
    fill : int or float or array-like
        The value(s) for padded borders. If `fill` is numerical type, RGB channels
        will be padded with single value. Otherwise `fill` must have same length
        as image channels, which resulted in padding with per-channel values.
    keep_ratio : bool
        If `True`, will keep output image the same aspect ratio as input.

    Returns
    -------
    mxnet.nd.NDArray
        Augmented image.
    tuple
        Tuple of (offset_x, offset_y, new_width, new_height)

    """
    if max_ratio <= 1:
        return src, (0, 0, src.shape[1], src.shape[0])

    h, w, c = src.shape
    ratio_x = random.uniform(1, max_ratio)
    if keep_ratio:
        ratio_y = ratio_x
    else:
        ratio_y = random.uniform(1, max_ratio)

    oh, ow = int(h * ratio_y), int(w * ratio_x)
    off_y = random.randint(0, oh - h)
    off_x = random.randint(0, ow - w)

    # make canvas
    if isinstance(fill, numeric_types):
        dst = nd.full(shape=(oh, ow, c), val=fill, dtype=src.dtype)
    else:
        fill = nd.array(fill, dtype=src.dtype, ctx=src.context)
        if not c == fill.size:
            raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
        dst = nd.tile(fill.reshape((1, c)), reps=(oh * ow, 1)).reshape((oh, ow, c))

    dst[off_y:off_y+h, off_x:off_x+w, :] = src
    return dst, (off_x, off_y, ow, oh) 

def resize_contain(src, size, fill=0):
    """Resize the image to fit in the given area while keeping aspect ratio.

    If both the height and the width in `size` are larger than
    the height and the width of input image, the image is placed on
    the center with an appropriate padding to match `size`.
    Otherwise, the input image is scaled to fit in a canvas whose size
    is `size` while preserving aspect ratio.

    Parameters
    ----------
    src : mxnet.nd.NDArray
        The original image with HWC format.
    size : tuple
        Tuple of length 2 as (width, height).
    fill : int or float or array-like
        The value(s) for padded borders. If `fill` is numerical type, RGB channels
        will be padded with single value. Otherwise `fill` must have same length
        as image channels, which resulted in padding with per-channel values.

    Returns
    -------
    mxnet.nd.NDArray
        Augmented image.
    tuple
        Tuple of (offset_x, offset_y, scaled_x, scaled_y)

    """
    h, w, c = src.shape
    ow, oh = size
    scale_h = oh / h
    scale_w = ow / w
    scale = min(min(scale_h, scale_w), 1)
    scaled_x = int(w * scale)
    scaled_y = int(h * scale)
    if scale < 1:
        src = mx.image.imresize(src, scaled_x, scaled_y)

    off_y = (oh - scaled_y) // 2 if scaled_y < oh else 0
    off_x = (ow - scaled_x) // 2 if scaled_x < ow else 0

    # make canvas
    if isinstance(fill, numeric_types):
        dst = nd.full(shape=(oh, ow, c), val=fill, dtype=src.dtype)
    else:
        fill = nd.array(fill, ctx=src.context)
        if not c == fill.size:
            raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
        dst = nd.repeat(fill, repeats=oh * ow).reshape((oh, ow, c))

    dst[off_y:off_y+scaled_y, off_x:off_x+scaled_x, :] = src
    return dst, (off_x, off_y, scaled_x, scaled_y) 

def random_expand(src, max_ratio=4, fill=0, keep_ratio=True):
    """Random expand original image with borders, this is identical to placing
    the original image on a larger canvas.

    Parameters
    ----------
    src : mxnet.nd.NDArray
        The original image with HWC format.
    max_ratio : int or float
        Maximum ratio of the output image on both direction(vertical and horizontal)
    fill : int or float or array-like
        The value(s) for padded borders. If `fill` is numerical type, RGB channels
        will be padded with single value. Otherwise `fill` must have same length
        as image channels, which resulted in padding with per-channel values.
    keep_ratio : bool
        If `True`, will keep output image the same aspect ratio as input.

    Returns
    -------
    mxnet.nd.NDArray
        Augmented image.
    tuple
        Tuple of (offset_x, offset_y, new_width, new_height)

    """
    if max_ratio <= 1:
        return src, (0, 0, src.shape[1], src.shape[0])

    h, w, c = src.shape
    ratio_x = random.uniform(1, max_ratio)
    if keep_ratio:
        ratio_y = ratio_x
    else:
        ratio_y = random.uniform(1, max_ratio)

    oh, ow = int(h * ratio_y), int(w * ratio_x)
    off_y = random.randint(0, oh - h)
    off_x = random.randint(0, ow - w)

    # make canvas
    if isinstance(fill, numeric_types):
        dst = nd.full(shape=(oh, ow, c), val=fill, dtype=src.dtype)
    else:
        fill = nd.array(fill, dtype=src.dtype, ctx=src.context)
        if not c == fill.size:
            raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
        dst = nd.tile(fill.reshape((1, c)), reps=(oh * ow, 1)).reshape((oh, ow, c))

    dst[off_y:off_y+h, off_x:off_x+w, :] = src
    return dst, (off_x, off_y, ow, oh) 

def resize_contain(src, size, fill=0):
    """Resize the image to fit in the given area while keeping aspect ratio.

    If both the height and the width in `size` are larger than
    the height and the width of input image, the image is placed on
    the center with an appropriate padding to match `size`.
    Otherwise, the input image is scaled to fit in a canvas whose size
    is `size` while preserving aspect ratio.

    Parameters
    ----------
    src : mxnet.nd.NDArray
        The original image with HWC format.
    size : tuple
        Tuple of length 2 as (width, height).
    fill : int or float or array-like
        The value(s) for padded borders. If `fill` is numerical type, RGB channels
        will be padded with single value. Otherwise `fill` must have same length
        as image channels, which resulted in padding with per-channel values.

    Returns
    -------
    mxnet.nd.NDArray
        Augmented image.
    tuple
        Tuple of (offset_x, offset_y, scaled_x, scaled_y)

    """
    h, w, c = src.shape
    ow, oh = size
    scale_h = oh / h
    scale_w = ow / w
    scale = min(min(scale_h, scale_w), 1)
    scaled_x = int(w * scale)
    scaled_y = int(h * scale)
    if scale < 1:
        src = mx.image.imresize(src, scaled_x, scaled_y)

    off_y = (oh - scaled_y) // 2 if scaled_y < oh else 0
    off_x = (ow - scaled_x) // 2 if scaled_x < ow else 0

    # make canvas
    if isinstance(fill, numeric_types):
        dst = nd.full(shape=(oh, ow, c), val=fill, dtype=src.dtype)
    else:
        fill = nd.array(fill, ctx=src.context)
        if not c == fill.size:
            raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
        dst = nd.repeat(fill, repeats=oh * ow).reshape((oh, ow, c))

    dst[off_y:off_y+scaled_y, off_x:off_x+scaled_x, :] = src
    return dst, (off_x, off_y, scaled_x, scaled_y) 

def random_expand(src, max_ratio=4, fill=0, keep_ratio=True):
    """Random expand original image with borders, this is identical to placing
    the original image on a larger canvas.

    Parameters
    ----------
    src : mxnet.nd.NDArray
        The original image with HWC format.
    max_ratio : int or float
        Maximum ratio of the output image on both direction(vertical and horizontal)
    fill : int or float or array-like
        The value(s) for padded borders. If `fill` is numerical type, RGB channels
        will be padded with single value. Otherwise `fill` must have same length
        as image channels, which resulted in padding with per-channel values.
    keep_ratio : bool
        If `True`, will keep output image the same aspect ratio as input.

    Returns
    -------
    mxnet.nd.NDArray
        Augmented image.
    tuple
        Tuple of (offset_x, offset_y, new_width, new_height)

    """
    if max_ratio <= 1:
        return src, (0, 0, src.shape[1], src.shape[0])

    h, w, c = src.shape
    ratio_x = random.uniform(1, max_ratio)
    if keep_ratio:
        ratio_y = ratio_x
    else:
        ratio_y = random.uniform(1, max_ratio)

    oh, ow = int(h * ratio_y), int(w * ratio_x)
    off_y = random.randint(0, oh - h)
    off_x = random.randint(0, ow - w)

    # make canvas
    if isinstance(fill, numeric_types):
        dst = nd.full(shape=(oh, ow, c), val=fill, dtype=src.dtype)
    else:
        fill = nd.array(fill, dtype=src.dtype, ctx=src.context)
        if not c == fill.size:
            raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
        dst = nd.tile(fill.reshape((1, c)), reps=(oh * ow, 1)).reshape((oh, ow, c))

    dst[off_y:off_y+h, off_x:off_x+w, :] = src
    return dst, (off_x, off_y, ow, oh) 

def resize_contain(src, size, fill=0):
    """Resize the image to fit in the given area while keeping aspect ratio.

    If both the height and the width in `size` are larger than
    the height and the width of input image, the image is placed on
    the center with an appropriate padding to match `size`.
    Otherwise, the input image is scaled to fit in a canvas whose size
    is `size` while preserving aspect ratio.

    Parameters
    ----------
    src : mxnet.nd.NDArray
        The original image with HWC format.
    size : tuple
        Tuple of length 2 as (width, height).
    fill : int or float or array-like
        The value(s) for padded borders. If `fill` is numerical type, RGB channels
        will be padded with single value. Otherwise `fill` must have same length
        as image channels, which resulted in padding with per-channel values.

    Returns
    -------
    mxnet.nd.NDArray
        Augmented image.
    tuple
        Tuple of (offset_x, offset_y, scaled_x, scaled_y)

    """
    h, w, c = src.shape
    ow, oh = size
    scale_h = oh / h
    scale_w = oh / w
    scale = min(min(scale_h, scale_w), 1)
    scaled_x = int(w * scale)
    scaled_y = int(h * scale)
    if scale < 1:
        src = mx.image.imresize(src, scaled_x, scaled_y)

    off_y = (oh - scaled_y) // 2 if scaled_y < oh else 0
    off_x = (ow - scaled_x) // 2 if scaled_x < ow else 0

    # make canvas
    if isinstance(fill, numeric_types):
        dst = nd.full(shape=(oh, ow, c), val=fill, dtype=src.dtype)
    else:
        fill = nd.array(fill, ctx=src.context)
        if not c == fill.size:
            raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
        dst = nd.repeat(fill, repeats=oh * ow).reshape((oh, ow, c))

    dst[off_y:off_y+scaled_y, off_x:off_x+scaled_x, :] = src
    return dst, (off_x, off_y, scaled_x, scaled_y)