# -*- coding: utf-8 -*-
from typing import Iterable, Optional, Tuple, Union
import numpy as np
from ..core import _config
from ..core._imperative_rt.core2 import apply
from ..core.ops import builtin
from ..core.tensor import megbrain_graph, utils
from ..core.tensor.utils import astensor1d
from ..tensor import Tensor
from .elemwise import floor
from .math import argsort
from .tensor import broadcast_to, concat, expand_dims, reshape, transpose
__all__ = [
"correlation",
"cvt_color",
"interpolate",
"nms",
"nvof",
"remap",
"roi_align",
"roi_pooling",
"warp_affine",
"warp_perspective",
]
[文档]def cvt_color(inp: Tensor, mode: str = ""):
r"""Convert images from one format to another
Args:
inp: input images.
mode: format mode.
Returns:
convert result.
Note:
There are different supported modes for different combinations of :attr:`~.Tensor.device` and :attr:`~.Tensor.dtype`.
x86/ARM:
float32:
"RGB2GRAY", "RGB2YUV", "YUV2RGB", "GRAY2RGB", "BGR2GRAY"
uint8:
"RGB2GRAY", "RGB2YUV", "YUV2RGB", "GRAY2RGB", "RGBA2RGB", "RGBA2BGR",
"RGBA2GRAY", "RGB2BGR", "BGR2GRAY", "BGR2RGB", "YUV2GRAY_NV21", "YUV2RGB_NV21",
"YUV2BGR_NV21", "YUV2GRAY_NV12", "YUV2RGB_NV12", "YUV2BGR_NV12", "YUV2GRAY_YV12",
"YUV2RGB_YV12", "YUV2BGR_YV12", "YUV2GRAY_YU12", "YUV2RGB_YU12", "YUV2BGR_YU12",
"YCrCb2RGB", "YCrCb2BGR", "BT601_YUV2RGB_NV21", "BT601_YUV2BGR_NV21", "BT601_YUV2RGB_NV12",
"BT601_YUV2BGR_NV12", "BT601_YUV2RGB_YV12", "BT601_YUV2BGR_YV12" ,"BT601_YUV2RGB_YU12",
"BT601_YUV2BGR_YU12"
CUDA:
float32:
"RGB2GRAY", "BGR2GRAY", "RGB2YUV", "YUV2RGB", "GRAY2RGB"
uint8:
"RGB2GRAY", "BGR2GRAY", "RGB2YUV", "YUV2RGB", "GRAY2RGB",
"YUV2GRAY_NV12", "YUV2GRAY_NV21", "YUV2GRAY_YU12"
"YUV2GRAY_YV12", "YUV2RGB_NV12", "YUV2RGB_NV21", "YUV2BGR_NV12"
"YUV2BGR_NV21", "YUV2RGB_YU12", "YUV2RGB_YV12", "YUV2BGR_YU12",
"YUV2BGR_YV12"
Examples:
>>> import numpy as np
>>> x = mge.tensor(np.array([[[[-0.58675045, 1.7526233, 0.10702174]]]]).astype(np.float32))
>>> y = F.vision.cvt_color(x, mode="RGB2GRAY")
>>> y.numpy()
array([[[[0.86555195]]]], dtype=float32)
"""
mode = mode.upper() if "YCrCb" not in mode else mode
assert mode in builtin.CvtColor.Mode.__dict__, "unspport mode for cvt_color"
mode = getattr(builtin.CvtColor.Mode, mode)
assert isinstance(mode, builtin.CvtColor.Mode)
op = builtin.CvtColor(mode=mode)
(out,) = apply(op, inp)
return out
[文档]def roi_pooling(
inp: Tensor,
rois: Tensor,
output_shape: Union[int, tuple, list],
mode: str = "max",
scale: float = 1.0,
) -> Tensor:
r"""Applies RoI (Region of Interest) pooling on input feature, as described in Faster RCNN.
.. seealso::
* `Region of interest pooling explained <https://deepsense.ai/region-of-interest-pooling-explained/>`_
* `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_
Args:
inp: the input tensor that represents the input feature with ``(n, c, h, w)`` shape.
rois: a tensor represents Regions of Interest with shape ``(K, 5)``, which means total ``K`` box coordinates in ``(idx, x1, y1, x2, y2)`` format where the regions will be taken from.
The coordinate including ``(x1, y1)`` and ``(x2, y2)`` must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
The first column ``idx`` should contain the index of the corresponding element in the input batch, i.e. a number in ``[0, n - 1]``.
mode: "max" or "average", the pooling mode to be used. Default: "max"
scale: It is a scale that maps output rois feature to input feature. For example, if the output is 224 * 224 image, and the input is a 112 * 112 feature map, then the scale should be set to 0.5. The default value is 1.0
Returns:
output tensor. ``(K, C, output_shape[0], output_shape[1])`` feature of rois.
Examples:
>>> import numpy as np
>>> np.random.seed(42)
>>> inp = Tensor(np.random.randn(1, 1, 128, 128))
>>> rois = Tensor(np.random.random((4, 5)))
>>> y = F.vision.roi_pooling(inp, rois, (2, 2))
>>> y.numpy()[0].round(decimals=4)
array([[[-0.1383, -0.1383],
[-0.5035, -0.5035]]], dtype=float32)
"""
assert mode.lower() in ["max", "average"], "only max/average mode is supported"
if isinstance(output_shape, int):
output_shape = (output_shape, output_shape)
op = builtin.ROIPooling(mode=mode, scale=scale)
result, _ = apply(
op, inp, rois, Tensor(output_shape, dtype="int32", device=inp.device)
)
return result
[文档]def correlation(
data1: Tensor,
data2: Tensor,
kernel_size: int = 1,
max_displacement: int = 1,
stride1: int = 1,
stride2: int = 1,
pad_size: int = 0,
is_multiply: bool = True,
) -> Tensor:
r"""Applies correlation to inputs.
Args:
data1: Input data1 to the correlation. format must be nchw
data2: Input data2 to the correlation. format must be nchw
kernel_size: int (non-negative), optional, default=1) – kernel size for Correlation must be an odd number
max_displacement: int (non-negative), optional, default=1) – Max displacement of Correlation
stride1: int (non-negative), optional, default=1) – stride1 quantize data1 globally
stride2: int (non-negative), optional, default=1) – stride2 quantize data2 within the neighborhood centered around data1
pad_size: int (non-negative), optional, default=0) – pad for Correlation
is_multiply: boolean, optional, default=True) – operation type is either multiplication or absolute difference
"""
# Currently correlation only support NCHW mode
format = "NCHW"
op = builtin.Correlation(
format=format,
kernel_size=kernel_size,
max_displacement=max_displacement,
stride1=stride1,
stride2=stride2,
pad_size=pad_size,
is_multiply=is_multiply,
)
result, *_ = apply(op, data1, data2)
return result
[文档]def roi_align(
inp: Tensor,
rois: Tensor,
output_shape: Union[int, tuple, list],
mode: str = "average",
spatial_scale: float = 1.0,
sample_points: Union[int, tuple, list] = 2,
aligned: bool = True,
) -> Tensor:
r"""Applies RoI (Region of Interest) align on input feature, as described in Mask R-CNN.
.. seealso::
* `RoIAlign <https://paperswithcode.com/method/roi-align>`_
* `Mask R-CNN <https://arxiv.org/abs/1703.06870v3>`_
Args:
inp: the input tensor that represents the input feature with ``(n, c, h, w)`` shape.
rois: a tensor represents Regions of Interest with shape ``(K, 5)``, which means total ``K`` box coordinates in ``(idx, x1, y1, x2, y2)`` format where the regions will be taken from.
The coordinate including ``(x1, y1)`` and ``(x2, y2)`` must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
The first column ``idx`` should contain the index of the corresponding element in the input batch, i.e. a number in ``[0, n - 1]``.
output_shape: ``(height, width)`` shape of output rois feature.
mode: "max" or "average", use max/average align just like max/average pooling. Default: "average"
spatial_scale: scale the input boxes by this number. Default: 1.0
sample_points: number of inputs samples to take for each output sample.
0 to take samples densely. Default: 2
aligned: wheather to align the input feature, with ``aligned=True``,
we first appropriately scale the ROI and then shift it by -0.5. Default: True
Returns:
output tensor.
Examples:
>>> import numpy as np
>>> np.random.seed(42)
>>> inp = Tensor(np.random.randn(1, 1, 128, 128))
>>> rois = Tensor(np.random.random((4, 5)))
>>> y = F.vision.roi_align(inp, rois, (2, 2))
>>> y.numpy()[0].round(decimals=4)
array([[[0.175 , 0.175 ],
[0.1359, 0.1359]]], dtype=float32)
"""
if inp.dtype != np.float32:
inp = inp.astype(np.float32)
mode = mode.lower()
assert mode in ["max", "average"], "only max/average mode is supported"
if isinstance(output_shape, int):
output_shape = (output_shape, output_shape)
pooled_height, pooled_width = output_shape
if isinstance(sample_points, int):
sample_points = (sample_points, sample_points)
sample_height, sample_width = sample_points
offset = 0.5 if aligned else 0.0
# Currently roi_align only support NCHW mode
format = "NCHW"
op = builtin.ROIAlign(
mode=mode,
format=format,
spatial_scale=spatial_scale,
offset=offset,
pooled_height=pooled_height,
pooled_width=pooled_width,
sample_height=sample_height,
sample_width=sample_width,
)
result, *_ = apply(op, inp, rois)
return result
[文档]def nms(
boxes: Tensor, scores: Tensor, iou_thresh: float, max_output: Optional[int] = None
) -> Tensor:
r"""Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union(IoU).
Args:
boxes: tensor of shape ``(N, 4)``; the boxes to perform nms on; each box is expected to be in ``(x1, y1, x2, y2)`` format.
iou_thresh: IoU threshold for overlapping.
scores: tensor of shape ``(N,)``, the score of boxes.
max_output: the maximum number of boxes to keep; it is optional if this operator is not traced
otherwise it required to be specified; if it is not specified, all boxes are kept.
Returns:
indices of the elements that have been kept by NMS, sorted by scores.
Note:
max_output should be specified and should have valid positive value under tracing.
Examples:
>>> import numpy as np
>>> x = np.zeros((100,4))
>>> np.random.seed(42)
>>> x[:,:2] = np.random.rand(100,2)*20
>>> x[:,2:] = np.random.rand(100,2)*20 + 100
>>> scores = Tensor(np.random.rand(100))
>>> inp = Tensor(x)
>>> F.vision.nms(inp, scores, iou_thresh=0.7)
Tensor([75 69], dtype=int32, device=xpux:0)
"""
assert (
boxes.ndim == 2 and boxes.shape[1] == 4
), "the expected shape of boxes is (N, 4)"
assert scores.ndim == 1, "the expected shape of scores is (N,)"
assert (
boxes.shape[0] == scores.shape[0]
), "number of boxes and scores are not matched"
boxes = boxes.detach()
scores = scores.detach()
sorted_idx = argsort(scores, descending=True)
boxes = boxes[sorted_idx]
if max_output is None:
max_output = boxes.shape[0]
op = builtin.NMSKeep(iou_thresh, max_output)
inp = (boxes.reshape(1, -1, 4),)
indices, count = apply(op, *inp)
indices = indices[0][: count[0]]
keep_inds = sorted_idx[indices]
return keep_inds
[文档]def remap(
inp: Tensor,
map_xy: Tensor,
border_mode: str = "replicate",
scalar: float = 0.0,
interp_mode: str = "linear",
) -> Tensor:
r"""Applies remap transformation to batched 2D images. Remap is an operation that relocates pixels in a image to another location in a new image.
The input images are transformed to the output images by the tensor ``map_xy``.
The output's H and W are same as ``map_xy``'s H and W.
Args:
inp: input image, its shape represents ``[b, c, in_h, in_w]``.
map_xy: transformation matrix, its shape shoule be ``[b, o_h, o_w, 2]``. The shape of output is determined by o_h and o_w.
For each element in output, its value is determined by inp and ``map_xy``.
``map_xy[..., 0]`` and ``map_xy[..., 1]`` are the positions of
the current element in inp, respectively. Therefore, their ranges are ``[0, in_w - 1]`` and ``[0, in_h - 1]``.
border_mode: pixel extrapolation method. Default: "replicate". Currently also support "constant", "reflect", "reflect_101", "wrap".
"replicate": repeatedly fills the edge pixel values of the duplicate image, expanding the new boundary pixel values with
the edge pixel values.
"constant": fills the edges of the image with a fixed numeric value.
scalar: value used in case of a constant border. Default: 0
interp_mode: interpolation methods. Default: "linear". Currently also support "nearest" mode.
Returns:
output tensor. [b, c, o_h, o_w]
Examples:
>>> import numpy as np
>>> inp_shape = (1, 1, 4, 4)
>>> inp = Tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
>>> map_xy_shape = (1, 2, 2, 2)
>>> map_xy = Tensor(np.array([[[1., 0.],[0., 1.]],
... [[0., 1.],[0., 1.]]],
... dtype=np.float32).reshape(map_xy_shape))
>>> out = F.vision.remap(inp, map_xy)
>>> out.numpy()
array([[[[1., 4.],
[4., 4.]]]], dtype=float32)
"""
format = "NCHW"
op = builtin.Remap(
imode=interp_mode, border_type=border_mode, format=format, scalar=scalar
)
assert isinstance(inp, (Tensor, megbrain_graph.VarNode)), "inp must be Tensor type"
(result,) = apply(op, inp, map_xy)
return result
[文档]def warp_affine(
inp: Tensor,
mat: Tensor,
out_shape: Union[Tuple[int, int], int, Tensor],
border_mode: str = "replicate",
border_val: float = 0.0,
format: str = "NHWC",
interp_mode: str = "linear",
) -> Tensor:
r"""Batched affine transformation on 2D images. Affine transformation is a linear transformation between two-dimensional coordinates.
Args:
inp: input image.
mat: `(batch, 2, 3)` transformation matrix.
out_shape: output tensor shape.
border_mode: pixel extrapolation method.
Default: "replicate". Currently "constant", "reflect",
"reflect_101", "isolated", "wrap", "replicate", "transparent" are supported.
border_val: value used in case of a constant border. Default: 0
format: NHWC" as default based on historical concerns,
"NCHW" is also supported. Default: "NHWC".
interp_mode: interpolation methods. Could be "linear", "nearest", "cubic", "area".
Default: "linear".
Returns:
output tensor.
Note:
Here all available options for params are listed,
however it does not mean that you can use all the combinations.
On different platforms, different combinations are supported.
``warp_affine`` only support forward inference, Please refer to ``warp_perspective`` if backward is needed.
"""
op = builtin.WarpAffine(
border_mode=border_mode,
border_val=border_val,
format=format,
imode=interp_mode,
)
out_shape = utils.astensor1d(out_shape, inp, dtype="int32", device=inp.device)
(result,) = apply(op, inp, mat, out_shape)
return result
[文档]def warp_perspective(
inp: Tensor,
mat: Tensor,
out_shape: Union[Tuple[int, int], int, Tensor],
mat_idx: Optional[Union[Iterable[int], Tensor]] = None,
border_mode: str = "replicate",
border_val: float = 0.0,
format: str = "NCHW",
interp_mode: str = "linear",
) -> Tensor:
r"""Applies perspective transformation to batched 2D images. A perspective transformation is a projection of a image onto a new view plane.
The input images are transformed to the output images by the transformation matrix:
.. math::
\text{output}(n, c, h, w) = \text{input} \left( n, c,
\frac{M_{00}w + M_{01}h + M_{02}}{M_{20}w + M_{21}h + M_{22}},
\frac{M_{10}w + M_{11}h + M_{12}}{M_{20}w + M_{21}h + M_{22}}
\right)
Optionally, we can set ``mat_idx`` to assign different transformations to the same image,
otherwise the input images and transformations should be one-to-one correnspondence.
Args:
inp: input image.
mat: ``(batch, 3, 3)`` transformation matrix.
out_shape: ``(h, w)`` size of the output image.
mat_idx: image batch idx assigned to each matrix. Default: None
border_mode: pixel extrapolation method.
Default: "replicate". Currently also support "constant", "reflect",
"reflect_101", "wrap".
border_val: value used in case of a constant border. Default: 0
format: NHWC" is also supported. Default: "NCHW".
interp_mode: interpolation methods.
Default: "linear". Currently only support "linear" mode.
Returns:
output tensor.
Note:
The transformation matrix is the inverse of that used by ``cv2.warpPerspective``.
Examples:
>>> import numpy as np
>>> inp_shape = (1, 1, 4, 4)
>>> x = Tensor(np.arange(16, dtype=np.float32).reshape(inp_shape))
>>> M_shape = (1, 3, 3)
>>> # M defines a translation: dst(1, 1, h, w) = rst(1, 1, h+1, w+1)
>>> M = Tensor(np.array([[1., 0., 1.],
... [0., 1., 1.],
... [0., 0., 1.]], dtype=np.float32).reshape(M_shape))
>>> out = F.vision.warp_perspective(x, M, (2, 2))
>>> out.numpy()
array([[[[ 5., 6.],
[ 9., 10.]]]], dtype=float32)
"""
if inp.dtype == np.float32:
mat = mat.astype("float32")
if inp.dtype == np.float16:
inp = inp.astype("float32")
op = builtin.WarpPerspective(
imode=interp_mode, bmode=border_mode, format=format, border_val=border_val
)
out_shape = astensor1d(out_shape, inp, dtype="int32", device=inp.device)
if mat_idx is not None:
mat_idx = astensor1d(mat_idx, inp, dtype="int32", device=inp.device)
(result,) = apply(op, inp, mat, mat_idx, out_shape)
return result
(result,) = apply(op, inp, mat, out_shape)
return result
[文档]def interpolate(
inp: Tensor,
size: Optional[Union[int, Tuple[int, int]]] = None,
scale_factor: Optional[Union[float, Tuple[float, float]]] = None,
mode: str = "bilinear",
align_corners: Optional[bool] = None,
) -> Tensor:
r"""Down/up samples the input tensor to either the given size or with the given scale_factor. ``size`` can not coexist with ``scale_factor``.
Args:
inp: input tensor.
size: the size of the output tensor. Default: None
scale_factor: scaling factor of the output tensor. Default: None
mode: interpolation methods, acceptable values are:
"bilinear", "linear", "bicubic" and "nearest". Default: "bilinear"
align_corners: This only has an effect when ``mode``
is "bilinear" or "linear". Geometrically, we consider the pixels of the input
and output as squares rather than points. If set to ``True``, the input
and output tensors are aligned by the center points of their corner
pixels, preserving the values at the corner pixels. If set to ``False``,
the input and output tensors are aligned by the corner points of their
corner pixels, and the interpolation uses edge value padding for
out-of-boundary values, making this operation *independent* of input size
Returns:
output tensor
Examples:
>>> import numpy as np
>>> x = Tensor(np.arange(1, 5, dtype=np.float32).reshape(1, 1, 2, 2))
>>> out = F.vision.interpolate(x, [4, 4], align_corners=False)
>>> out.numpy()
array([[[[1. , 1.25, 1.75, 2. ],
[1.5 , 1.75, 2.25, 2.5 ],
[2.5 , 2.75, 3.25, 3.5 ],
[3. , 3.25, 3.75, 4. ]]]], dtype=float32)
>>> out2 = F.vision.interpolate(x, scale_factor=2.)
>>> np.testing.assert_allclose(out.numpy(), out2.numpy())
"""
mode = mode.lower()
if mode not in ["bilinear", "linear", "bicubic", "nearest"]:
raise ValueError("unsupported interpolate mode: {}".format(mode))
if mode not in ["bilinear", "linear"]:
if align_corners is not None:
raise ValueError(
"align_corners option can only be set in the bilinear/linear interpolating mode"
)
else:
if align_corners is None:
align_corners = False
if mode == "linear":
inp = expand_dims(inp, 3)
if inp.ndim != 4:
raise ValueError("shape of input tensor must correspond to the operartion mode")
def get_dsize(scale_factor):
if isinstance(scale_factor, (float, int)):
scale_factor = float(scale_factor)
if mode == "linear":
scale_factor = (scale_factor, float(1))
else:
scale_factor = (scale_factor, scale_factor)
else:
if mode == "linear":
raise ValueError(
"under linear mode, scale_factor can only be single value"
)
assert len(scale_factor) == 2, "shape of scale_factor must be equal to (2, )"
assert isinstance(scale_factor[0], float) and isinstance(
scale_factor[1], float
), "scale_factor must be float type"
dsize = tuple(
floor(
Tensor(
inp.shape[i + 2] * scale_factor[i],
dtype="float32",
device=inp.device,
)
)
for i in range(2)
)
dsize = concat([dsize[0], dsize[1]], axis=0)
return dsize
if size is None:
if scale_factor is None:
raise ValueError("scale_factor must not be None when size is None")
dsize = get_dsize(scale_factor)
else:
if scale_factor is not None:
raise ValueError("scale_factor must be None when size is provided")
if isinstance(size, int):
size = (size, 1)
else:
if mode == "linear":
raise ValueError("under linear mode, size can only be single value")
dsize = size
if not align_corners:
# fastpath for interpolate
mode_map = {
"linear": "linear",
"bilinear": "linear",
"nearest": "nearest",
"bicubic": "cubic",
}
if inp.dtype == np.float16:
inp = inp.astype("float32")
# Currently resize only support NCHW mode
format = "NCHW"
op = builtin.Resize(imode=mode_map[mode], format=format)
shape = astensor1d(dsize, inp, dtype="int32", device=inp.device)
(ret,) = apply(op, inp, shape)
else:
assert mode in [
"linear",
"bilinear",
], "align_corners only support linear or bilinear mode"
oh, ow = dsize[0], dsize[1]
ih, iw = inp.shape[2], inp.shape[3]
hscale = (ih - 1.0) / (oh - 1.0)
wscale = 1.0 * iw / ow
if mode != "linear":
wscale = (iw - 1.0) / (ow - 1.0)
row0 = concat(
[
Tensor(wscale, dtype="float32", device=inp.device),
Tensor([0, 0], dtype="float32", device=inp.device),
],
axis=0,
).reshape(1, 3)
zeros = Tensor([0], dtype="float32", device=inp.device)
row1 = concat(
[zeros, Tensor(hscale, dtype="float32", device=inp.device), zeros], axis=0,
).reshape(1, 3)
weight = concat(
[row0, row1, Tensor([[0, 0, 1]], dtype="float32", device=inp.device)],
axis=0,
).reshape(1, 3, 3)
weight = broadcast_to(weight, (inp.shape[0], 3, 3))
ret = warp_perspective(inp, weight, dsize, interp_mode="linear")
if mode == "linear":
ret = reshape(ret, ret.shape[0:3])
return ret
[文档]def nvof(src: Tensor, precision: int = 1) -> Tensor:
r"""Implements NVIDIA Optical Flow SDK.
Args:
src: input tensor with shape (n, t, h, w, c4) and unit8 dtype.
precision: 0:NV_OF_PERF_LEVEL_SLOW 1:NV_OF_PERF_LEVEL_MEDIUM 2:NV_OF_PERF_LEVEL_FAST.
Returns:
output tensor with shape: ``(n, t-1, (h+out_grid_size-1)//out_grid_size, (w+out_grid_size-1)//out_grid_size, c2)``.
By default, out_grid_size = 4. dtype: int16.
"""
assert src.ndim == 5 and src.shape[4] == 4
src = src.detach()
op = builtin.NvOf(precision=precision)
return apply(op, src)[0]