inp: tensor that represents the input feature, `(N, C, H, W)` images.
rois: `(K, 5)` boxes. First column is the index into N. The other 4 columns are xyxy.
output_shape: `(height, width)` of output rois feature.
mode: "max" or "average", use max/average align just like max/average pooling. Default: "max"
scale: scale the input boxes by this number. Default: 1.0
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:
``K, C, output_shape[0], output_shape[1])`` feature of rois.
output tensor. ``(K, C, output_shape[0], output_shape[1])`` feature of rois.
Examples:
>>> import numpy as np
...
...
@@ -172,17 +178,24 @@ def roi_align(
sample_points:Union[int,tuple,list]=2,
aligned:bool=True,
)->Tensor:
r"""Applies roi align on input feature.
r"""Applies RoI (Region of Interest) align on input feature, as described in Mask R-CNN.
inp: tensor that represents the input feature, shape is `(N, C, H, W)`.
rois: `(N, 5)` boxes. First column is the box index. The other 4 columns are ``xyxy``.
output_shape: `(height, width)` shape of output rois feature.
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`,
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:
...
...
@@ -233,9 +246,9 @@ def nms(
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.
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.
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.
...
...
@@ -287,27 +300,26 @@ def remap(
scalar:float=0.0,
interp_mode:str="linear",
)->Tensor:
r"""Applies remap transformation to batched 2D images.
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.
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
map_xy: transformation matrix, its shape represents [batch_size, H, W, 2]. map_xy's H and W are the same as output's H and W.
For each output location output[n, h, w], the vector map_xy[n, h, w] specifies input pixel location x and y, which are
used to interpolate the output value output[n, h, w]. In the case of 2D inputs, map_xy[n, h, w] specifies the x, y pixel
locations for interpolating output[n, h, w], map_xy specifies the sampling pixel locations normalized by the inp spatial
dimensions. Therefore, it should have most values in the range of [0, h - 1) and [0, w - 1).
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
"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.
output tensor. [b, c, o_h, o_w]
Examples:
>>> import numpy as np
...
...
@@ -341,7 +353,7 @@ def warp_affine(
format:str="NHWC",
interp_mode:str="linear",
)->Tensor:
r"""Batched affine transform on 2D images.
r"""Batched affine transformation on 2D images. Affine transformation is a linear transformation between two-dimensional coordinates.
Args:
inp: input image.
...
...
@@ -386,7 +398,7 @@ def warp_perspective(
format:str="NCHW",
interp_mode:str="linear",
)->Tensor:
r"""Applies perspective transformation to batched 2D images.
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:
